LongBench v2 / 66f3c1ab821e116aacb2ead1 / Which of the following statement is false according to the three materials?

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CHAPTER 7: 
AI Policy and  
National Strategies
Artificial Intelligence
Index Report 2021


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Index Report 2021
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
Overview	
3
Chapter Highlights	
4
7.1 
NATIONAL AND REGIONAL 	
   
     AI STRATEGIES	
5
Published Strategies	
6
	
2017	
6
	
2018	
7
	
2019	
9
	
2020	
11
Strategies in Development 	
   
(as of December 2020)	
12
	
Strategies in Public Consultation	
12
Strategies Announced	
13
Highlight: National AI Strategies  
and Human Rights	
14
7.2 INTERNATIONAL 
     COLLABORATION ON AI	
15
Intergovernmental Initiatives	
15
	
Working Group	
15
	
Summits and Meetings	
16
Bilateral Agreements	
16
7.3 U.S. PUBLIC INVESTMENT IN AI	
17
Federal Budget for Non-Defense AI R&D	 17
U.S. Department of Defense  
Budget Request	
18
U.S. Government Contract Spending	
19
	
Total Contract Spending	
19
	
Contract Spending by  
	
Department and Agency	
19
7.4 AI AND POLICYMAKING	
21
Legislation Records on AI	
21
	
U.S. Congressional Record	
22
	
Mentions of AI and ML in  
	
Congressional/Parliamentary 
	
Proceedings	
22
Central Banks	
24
U.S. AI Policy Papers	
26
APPENDIX	
27
Chapter Preview
CHAPTER 7:
ACCESS THE PUBLIC DATA


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Index Report 2021
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
Overview
OVERVIEW
AI is set to shape global competitiveness over the coming decades, promising 
to grant early adopters a significant economic and strategic advantage. To 
date, national governments and regional and intergovernmental organizations 
have raced to put in place AI-targeted policies to maximize the promise of the 
technology while also addressing its social and ethical implications. 
This chapter navigates the landscape of AI policymaking and tracks efforts taking 
place on the local, national, and international levels to help promote and govern AI 
technologies. It begins with an overview of national and regional AI strategies and 
then reviews activities on the intergovernmental level. The chapter then takes a 
closer look at public investment in AI in the United States as well as how legislative 
bodies, central banks, and nongovernmental organizations are responding to the 
growing need to institute a policy framework for AI technologies.


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CHAPTER 7:
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CHAPTER
HIGHLIGHTS
CHAPTER HIGHLIGHTS
• 
Since Canada published the world’s first national AI strategy in 2017, more than 30 other 
countries and regions have published similar documents as of December 2020.
• 
The launch of the Global Partnership on AI (GPAI) and Organisation for Economic  
Co-operation and Development (OECD) AI Policy Observatory and Network of Experts  
on AI in 2020 promoted intergovernmental efforts to work together to support the 
development of AI for all.
• 
In the United States, the 116th Congress was the most AI-focused congressional session in 
history. The number of mentions of AI by this Congress in legislation, committee reports, and 
Congressional Research Service (CRS) reports is more than triple that of the 115th Congress. 


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To guide and foster the development of AI, countries and regions around the world are establishing strategies and 
initiatives to coordinate governmental and intergovernmental efforts. Since Canada published the world’s first national 
AI strategy in 2017, more than 30 other countries and regions have published similar documents as of December 2020. 
7.1 NATIONAL AND REGIONAL 
AI STRATEGIES
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
This section presents an overview of select national and regional AI strategies from around the world, including details on 
the strategies for G20 countries, Estonia, and Singapore as well as links to strategy documents for many others. Sources 
include websites of national or regional governments, the OECD AI Policy Observatory (OECD.AI), and news coverage. “AI 
strategy” is defined as a policy document that communicates the objective of supporting the development of AI while also 
maximizing the benefits of AI for society. Excluded are broader innovation or digital strategy documents which do not focus 
predominantly on AI, such as Brazil’s E-Digital Strategy and Japan’s Integrated Innovation Strategy.
COUNTRIES  
WITH PUBLISHED 
AI STRATEGIES: 32
COUNTRIES  
DEVELOPING  
AI STRATEGIES: 22 


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Published Strategies
2017
Canada
• 
AI Strategy: Pan Canadian AI Strategy
• 
Responsible Organization: Canadian Institute for 
Advanced Research (CIFAR) 
• 
Highlights: The Canadian strategy emphasizes 
developing Canada’s future AI workforce, supporting major 
AI innovation hubs and scientific research, and positioning 
the country as a thought leader in the economic, ethical, 
policy, and legal implications of artificial intelligence. 
• 
Funding (December 2020 conversion rate): CAD 125 
million (USD 97 million)
•  In November 2020, CIFAR published its most recent 
annual report, titled “AICAN,” which tracks progress on 
implementing its national strategy, which highlighted 
substantial growth in Canada’s AI ecosystem, as well 
as research and activities related to healthcare and AI’s 
impact on society, among other outcomes of the strategy.  
China
• 
AI Strategy: A Next Generation Artificial Intelligence 
Development Plan
• 
Responsible Organization: State Council for the People’s 
Republic of China
• 
Highlights: China’s AI strategy is one of the most 
comprehensive in the world. It encompasses areas 
including R&D and talent development through 
education and skills acquisition, as well as ethical norms 
and implications for national security. It sets specific 
targets, including bringing the AI industry in line with 
competitors by 2020; becoming the global leader in fields 
such as unmanned aerial vehicles (UAVs), voice and 
image recognition, and others by 2025; and emerging as 
the primary center for AI innovation by 2030.
• 
Funding: N/A
• 
Recent Updates: China established a New Generation 
AI Innovation and Development Zone in February 2019 
and released the “Beijing AI Principles” in May 2019 with 
a multi-stakeholder coalition consisting of academic 
institutions and private-sector players such as Tencent 
and Baidu. 
Japan
• 
AI Strategy: Artificial Intelligence Technology Strategy
• 
Responsible Organization: Strategic Council for AI 
Technology
• 
Highlights: The strategy lays out three discrete phases of 
AI development. The first phase focuses on the utilization 
of data and AI in related service industries, the second 
on the public use of AI and the expansion of service 
industries, and the third on creating an overarching 
ecosystem where the various domains are merged. 
• 
Funding: N/A
• 
Recent Updates: In 2019, the Integrated Innovation 
Strategy Promotion Council launched another AI strategy, 
aimed at taking the next step forward in overcoming 
issues faced by Japan and making use of the country’s 
strengths to open up future opportunities.
Others
Finland: Finland’s Age of Artificial Intelligence
United Arab Emirates: UAE Strategy for Artificial 
Intelligence
7.1 NATIONAL 
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Published Strategies
2018
European Union 
• 
AI Strategy: Coordinated Plan on Artificial Intelligence
• 
Responsible Organization: European Commission 
• 
Highlights: This strategy document outlines the 
commitments and actions agreed on by EU member 
states, Norway, and Switzerland to increase investment 
and build their AI talent pipeline. It emphasizes the value 
of public-private partnerships, creating European data 
spaces, and developing ethics principles.
• 
Funding (December 2020 conversation rate): At least 
EUR 1 billion (USD 1.1 billion) per year for AI research and 
at least EUR 4.9 billion (USD 5.4 billion) for other aspects 
of the strategy
• 
Recent updates: A first draft of the ethics guidelines was 
released in June 2018, followed by an updated version in 
April 2019.
France
• 
AI Strategy: AI for Humanity: French Strategy for Artificial 
Intelligence
• 
Responsible Organizations: Ministry for Higher 
Education, Research and Innovation; Ministry of Economy 
and Finance; Directorate General for Enterprises; Public 
Health Ministry; Ministry of the Armed Forces; National 
Research Institute for Digital Sciences; Interministerial 
Director of the Digital Technology and the Information 
and Communication System 
• 
Highlights: The main themes include developing 
an aggressive data policy for big data; targeting four 
strategic sectors, namely health care, environment, 
transport, and defense; boosting French efforts in 
research and development; planning for the impact of AI 
on the workforce; and ensuring inclusivity and diversity 
within the field.
• 
Funding (December 2020 conversion rate): EUR 1.5 
billion (USD 1.8 billion) up to 2022
• 
Recent Updates: The French National Research Institute 
for Digital Sciences (Inria) has committed to playing a 
central role in coordinating the national AI strategy and 
will report annually on its progress.
Germany
• 
AI Strategy: AI Made in Germany
• 
Responsible Organizations: Federal Ministry of 
Education and Research; Federal Ministry for Economic 
Affairs and Energy; Federal Ministry of Labour and Social 
Affairs
• 
Highlights: The focus of the strategy is on cementing 
Germany as a research powerhouse and strengthening 
the value of its industries. There is also an emphasis 
on the public interest and working to better the lives of 
people and the environment.
• 
Funding (December 2020 conversion rate): EUR 500 
million (USD 608 million) in the 2019 budget and EUR 
3 billion (USD 3.6 billion) for the implementation up to 
2025
• 
Recent Updates: In November 2019, the government 
published an interim progress report on the Germany AI 
strategy.
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AND REGIONAL 
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2018 (continued)
India
• 
AI Strategy: National Strategy on  
Artificial Intelligence: #AIforAll
• 
Responsible Organization: National Institution for 
Transforming India (NITI Ayog)
• 
Highlights: The Indian strategy focuses on both 
economic growth and ways to leverage AI to increase 
social inclusion, while also promoting research to 
address important issues such as ethics, bias, and 
privacy related to AI. The strategy emphasizes sectors 
such as agriculture, health, and education, where public 
investment and government initiative are necessary.
• 
Funding (December 2020 conversion rate): INR 7000 
crore (USD 949 million)
• 
Recent Updates: In 2019, the Ministry of Electronics and 
Information Technology released its own proposal to 
set up a national AI program with an allocated INR 400 
crore (USD 54 million). The Indian government formed 
a committee in late 2019 to push for an organized AI 
policy and establish the precise functions of government 
agencies to further India’s AI mission.
Mexico
• 
AI Strategy: Artificial Intelligence Agenda MX  
(2019 agenda-in-brief version)
• 
Responsible Organization: IA2030Mx, Economía
• 
Highlights: As Latin America’s first strategy, the Mexican 
strategy focuses on developing a strong governance 
framework, mapping the needs of AI in various industries, 
and identifying governmental best practices with an 
emphasis on developing Mexico’s AI leadership.
• 
Funding: N/A
• 
Recent Updates: According to the Inter-American 
Development Bank’s recent fAIr LAC report, Mexico is in 
the process of establishing concrete AI policies to further 
implementation.
United Kingdom
• 
AI Strategy: Industrial Strategy: Artificial Intelligence 
Sector Deal
• 
Responsible Organization: Office for Artificial 
Intelligence (OAI)
• 
Highlights: The U.K. strategy emphasizes a strong 
partnership between business, academia, and the 
government and identifies five foundations for a 
successful industrial strategy: becoming the world’s most 
innovative economy, creating jobs and better earnings 
potential, infrastructure upgrades, favorable business 
conditions, and building prosperous communities 
throughout the country. 
• 
Funding (December 2020 conversion rate): GBP 950 
million (USD 1.3 billion)
• 
Recent Updates: Between 2017 and 2019, the U.K.’s 
Select Committee on AI released an annual report on the 
country’s progress. In November 2020, the government 
announced a major increase in defense spending of 
GBP 16.5 billion (USD 21.8 billion) over four years, with 
a major emphasis on AI technologies that promise to 
revolutionize warfare.
Others
Sweden: National Approach to Artificial Intelligence
Taiwan: Taiwan AI Action Plan
7.1 NATIONAL 
AND REGIONAL 
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Published Strategies
2019
Estonia
• 
AI Strategy: National AI Strategy 2019–2021
• 
Responsible Organization: Ministry of Economic Affairs 
and Communications (MKM)
• 
Highlights: The strategy emphasizes actions necessary 
for both the public and private sectors to take to increase 
investment in AI research and development, while also 
improving the legal environment for AI in Estonia. In 
addition, it hammers out the framework for a steering 
committee that will oversee the implementation and 
monitoring of the strategy.
• 
Funding (December 2020 conversion rate): EUR 10 
million (USD 12 million) up to 2021
• 
Recent Updates: The Estonian government released an 
update on the AI taskforce in May 2019.
Russia
• 
AI Strategy: National Strategy for the Development of 
Artificial Intelligence
• 
Responsible Organizations: Ministry of Digital 
Development, Communications and Mass Media; 
Government of the Russian Federation
• 
Highlights: The Russian AI strategy places a strong 
emphasis on its national interests and lays down 
guidelines for the development of an “information 
society” between 2017 and 2030. These include a 
national technology initiative, departmental projects 
for federal executive bodies, and programs such as the 
Digital Economy of the Russian Federation, designed to 
implement the AI framework across sectors. 
• 
Funding: N/A
• 
Recent Updates: In December 2020, Russian president 
Vladmir Putin took part in the Artificial Intelligence 
Journey Conference, where he presented four ideas for AI 
policies: establishing experimental legal frameworks for 
the use of AI, developing practical measures to introduce 
AI algorithms, providing neural network developers with 
competitive access to big data, and boosting private 
investment in domestic AI industries.
Singapore
• 
AI Strategy: National Artificial Intelligence Strategy
• 
Responsible Organization: Smart Nation and Digital 
Government Office (SNDGO)
• 
Highlights: Launched by Smart Nation Singapore, a 
government agency that seeks to transform Singapore’s 
economy and usher in a new digital age, the strategy 
identifies five national AI projects in the following fields: 
transport and logistics, smart cities and estates, health 
care, education, and safety and security.
• 
Funding (December 2020 conversion rate): While the 
2019 strategy does not mention funding, in 2017 the 
government launched its national program, AI Singapore, 
with a pledge to invest SGD 150 million (USD 113 million) 
over five years. 
• 
Recent Updates: In November 2020, SNDGO published 
its inaugural annual update on the Singaporean 
government’s data protection efforts. It describes the 
measures taken to date to strengthen public sector data 
security and to safeguard citizens’ private data.
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2019 (continued)
United States
• 
AI Strategy: American AI Initiative
• 
Responsible Organization: The White House
• 
Highlights: The American AI Initiative prioritizes 
the need for the federal government to invest in AI 
R&D, reduce barriers to federal resources, and ensure 
technical standards for the safe development, testing, 
and deployment of AI technologies. The White House 
also emphasizes developing an AI-ready workforce and 
signals a commitment to collaborating with foreign 
partners while promoting U.S. leadership in AI. The 
initiative, however, lacks specifics on the program’s 
timeline, whether additional research will be dedicated 
to AI development, and other practical considerations.
• 
Funding: N/A 
• 
Recent Updates: The U.S. government released its 
year one annual report in February 2020, followed in 
November by the first guidance memorandum for federal 
agencies on regulating artificial intelligence applications 
in the private sector, including principles that encourage 
AI innovation and growth and increase public trust and 
confidence in AI technologies. The National Defense 
Authorization Act (NDAA) for Fiscal Year 2021 called for a 
National AI Initiative to coordinate AI research and policy 
across the federal government.
South Korea
• 
AI Strategy: National Strategy for Artificial Intelligence
• 
Responsible Organization: Ministry of Science, ICT and 
Future Planning (MSIP) 
• 
Highlights: The Korean strategy calls for plans to 
facilitate the use of AI by businesses and to streamline 
regulations to create a more favorable environment for 
the development and use of AI and other new industries. 
The Korean government also plans to leverage its 
dominance in the global supply of memory chips to build 
the next generation of smart chips by 2030.
• 
Funding (December 2020 conversion rate):  
KRW 2.2 trillion (USD 2 billion)
• 
Recent Updates: N/A
Others
Colombia: National Policy for Digital Transformation 
and Artificial Intelligence
Czech Republic: National Artificial Intelligence 
Strategy of the Czech Republic
Lithuania: Lithuanian Artificial Intelligence Strategy: A 
Vision for the Future
Luxembourg: Artificial Intelligence: A Strategic Vision 
for Luxembourg
Malta: Malta: The Ultimate AI Launchpad
Netherlands: Strategic Action Plan for Artificial 
Intelligence
Portugal: AI Portugal 2030
Qatar: National Artificial Intelligence for Qatar
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Published Strategies
2020
Indonesia
• 
AI Strategy: National Strategy for the Development of 
Artificial Intelligence (Stranas KA)
• 
Responsible Organizations: Ministry of Research 
and Technology (Menristek), National Research and 
Innovation Agency (BRIN), Agency for the Assessment and 
Application of Technology (BPPT)
• 
Strategy Highlights: The Indonesian strategy aims 
to guide the country in developing AI between 2020 
and 2045. It focuses on education and research, health 
services, food security, mobility, smart cities, and public 
sector reform.
• 
Funding: N/A
• 
Recent Updates: None
Saudi Arabia
• 
AI Strategy: National Strategy on Data and AI (NSDAI)
• 
Responsible Organization: Saudi Data and Artificial 
Intelligence Authority (SDAIA)
• 
Highlights: As part of an effort to diversify the country’s 
economy away from oil and boost the private sector, the 
NSDAI aims to accelerate AI development in five critical 
sectors: health care, mobility, education, government, 
and energy. By 2030, Saudi Arabia intends to train 20,000 
data and AI specialists, attract USD 20 billion in foreign 
and local investment, and create an environment that 
will attract at least 300 AI and data startups. 
• 
Funding: N/A
• 
Recent Updates: During the summit where the 
Saudi government released its strategy, the country’s 
National Center for Artificial Intelligence (NCAI) signed 
collaboration agreements with China’s Huawei and 
Alibaba Cloud to design AI-related Arabic-language 
systems.
Others
Hungary: Hungary’s Artificial Intelligence Strategy
Norway: National Strategy for Artificial Intelligence
Serbia: Strategy for the Development of Artificial 
Intelligence in the Republic of Serbia for the Period 
2020–2025
Spain: National Artificial Intelligence Strategy
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Strategies in Development
 (AS OF DECEMBER 2020)
Strategies in Public Consultation
Brazil
• 
AI Strategy Draft: Brazilian Artificial Intelligence Strategy
• 
Responsible Organization: Ministry of Science, 
Technology and Innovation (MCTI)
• 
Highlights: Brazil’s national AI strategy was announced 
in 2019 and is currently in the public consultation stage. 
According to the OECD, the strategy aims to cover 
relevant topics bearing on AI, including its impact on the 
economy, ethics, development, education, and jobs, and 
to coordinate specific public policies addressing such 
issues.
• 
Funding: N/A
• 
Recent Updates: In October 2020, the country’s largest 
research facility dedicated to AI was launched in 
collaboration with IBM, the University of São Paulo, and 
the São Paulo Research Foundation.
Italy
• 
AI Strategy Draft: Proposal for an Italian Strategy for 
Artificial Intelligence
• 
Responsible Organization: Ministry of Economic 
Development (MISE) 
• 
Highlights: This document provides the proposed 
strategy for the sustainable development of AI, aimed 
at improving Italy’s competitiveness in AI. It focuses on 
improving AI-based skills and competencies, fostering AI 
research, establishing a regulatory and ethical framework 
to ensure a sustainable ecosystem for AI, and developing 
a robust data infrastructure to fuel these developments.
• 
Funding (December 2020 conversion rate): EUR 1 
billion (USD 1.1 billion) through 2025 and expected 
matching funds from the private sector, bringing the total 
investment to EUR 2 billion.
• 
Recent Updates: None
Others
Cyprus: National Strategy for Artificial Intelligence
Ireland: National Irish Strategy on Artificial Intelligence
Poland: Artificial Intelligence Development Policy in 
Poland
Uruguay: Artificial Intelligence Strategy for Digital 
Government
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Strategies Announced
Argentina
• 
Related Document: N/A
• 
Responsible Organization: Ministry of Science, 
Technology and Productive Innovation (MINCYT)
• 
Status: Argentina’s AI plan is a part of the Argentine 
Digital Agenda 2030 but has not yet been published. It is 
intended to cover the decade between 2020 and 2030, 
and reports indicate that it has the potential to reap huge 
benefits for the agricultural sector.
Australia
• 
Related Documents: Artificial Intelligence Roadmap /  
An AI Action Plan for all Australians
• 
Responsible Organizations: Commonwealth Scientific 
and Industrial Research Organisation (CSIRO), Data 61, 
and the Australian government
• 
Status: The Australian government published a road 
map in 2019 (in collaboration with the national science 
agency, CSIRO) and a discussion paper of an AI action 
plan in 2020 as frameworks to develop a national 
AI strategy. In its 2018–19 budget, the Australian 
government earmarked AUD 29.9 million (USD 22.2 
million [December 2020 conversation rate]) over four 
years to strengthen the country’s capabilities in AI and 
machine learning (ML). In addition, CSIRO published a 
research paper on Australia’s AI Ethics Framework in 2019 
and launched a public consultation, which is expected to 
produce a forthcoming strategy document.
Turkey
• 
Related Document: N/A
• 
Responsible Organizations: Presidency of the Republic 
of Turkey Digital Transformation Office; Ministry of 
Industry and Technology; Scientific and Technological 
Research Council of Turkey; Science, Technology and 
Innovation Policies Council
• 
Status: The strategy has been announced but not yet 
published. According to media sources, it will focus 
on talent development, scientific research, ethics and 
inclusion, and digital infrastructure.
Others
Austria: Artificial Intelligence Mission Austria  
(official report)
Bulgaria: Concept for the Development of Artificial 
Intelligence in Bulgaria Until 2030 (concept document)
Chile: National AI Policy (official announcement)
Israel: National AI Plan (news article)
Kenya: Blockchain and Artificial Intelligence Taskforce 
(news article) 
Latvia: On the Development of Artificial Intelligence 
Solutions (official report)
Malaysia: National Artificial Intelligence (Al) Framework 
(news article)
New Zealand: Artificial Intelligence: Shaping a Future 
New Zealand (official report)
Sri Lanka: Framework for Artificial Intelligence (news 
article)
Switzerland: Artificial Intelligence (official guidelines)
Tunisia: National Artificial Intelligence Strategy (task 
force announced)
Ukraine: Concept of Artificial Intelligence Development 
in Ukraine AI (concept document)
Vietnam: Artificial Intelligence Development Strategy 
(official announcement)
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Read more on AI national strategies:
• 
Tim Dutton: An Overview of National AI Strategies
• 
Organisation for Economic Co-operation and Development: OECD AI Policy Observatory
• 
Canadian Institute for Advanced Research: Building an AI World, Second Edition
• 
Inter-American Development Bank: Artificial Intelligence for Social Good in Latin America and the Caribbean:  
The Regional Landscape and 12 Country Snapshots
7.1 NATIONAL 
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National AI Strategies and Human Rights
Table 7.1.1: Mapping human rights 
referenced in national AI strategies
HUMAN RIGHTS 
MENTIONED
STATES/REGIONAL 
ORGANIZATIONS
The right to privacy
Australia, Belgium, China, 
Czech Republic, Germany, 
India, Italy, Luxembourg, Malta, 
Netherlands, Norway, Portugal, 
Qatar, South Korea, United 
States
The right 
to equality/
nondiscrimination
Australia, Belgium, Czech 
Republic, Denmark, Estonia, EU, 
France, Germany, Italy, Malta, 
Netherlands, Norway
The right to an 
effective remedy
Australia (responsibility 
and ability to hold humans 
responsible), Denmark, Malta, 
Netherlands
The rights to 
freedom of thought, 
expression, 
and access to 
information
France, Netherlands,  
Russia
The right to work
France, Russia
In 2020, Global Partners Digital and Stanford’s 
Global Digital Policy Incubator published a 
report examining governments’ national AI 
strategies from a human rights perspective, 
titled “National Artificial Intelligence Strategies 
and Human Rights: A Review.” The report 
assesses the extent to which governments 
and regional organizations have incorporated 
human rights considerations into their national 
AI strategies and made recommendations to 
policymakers looking to develop or review AI 
strategies in the future. 
The report found that among the 30 states and 
two regional strategies (from the European 
Union and the Nordic-Baltic states), a number 
of strategies refer to the impact of AI on human 
rights, with the right to privacy as the most 
commonly mentioned, followed by equality 
and nondiscrimination (Table 6.1.1). However, 
very few strategy documents provide deep 
analysis or concrete assessment of the impact 
of AI applications on human rights. Specifics 
as to how and the depth to which human 
rights should be protected in the context of 
AI is largely missing, in contrast to the level of 
specificity on other issues such as economic 
competitiveness and innovation advantage. 


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Given the scale of the opportunities and the challenges 
presented by AI, a number of international efforts have 
recently been announced that aim to develop multilateral 
AI strategies. This section provides an overview of those 
international initiatives from governments committed to 
working together to support the development of AI for all. 
These multilateral initiatives on AI suggest that 
organizations are taking a variety of approaches to 
tackle the practical applications of AI and scale those 
solutions for maximum global impact. Many countries 
turn to international organizations for global AI norm 
formulation, while others engage in partnerships or 
bilateral agreements. Among the topics under discussion, 
the ethics of AI—or the ethical challenges raised by current 
and future applications of AI—stands out as a particular 
focus area for intergovernmental efforts. 
Countries such as Japan, South Korea, the United 
Kingdom, the United States, and members of the European 
Union are active participants of intergovernmental 
efforts on AI. A major AI powerhouse, China, on the other 
hand, has opted to engage in a number of science and 
technology bilateral agreements that stress cooperation 
on AI as part of the Digital Silk Road under the Belt 
and Road (BRI) initiative framework. For example, AI is 
mentioned in China’s economic cooperation under the BRI 
Initiative with the United Arab Emirates. 
INTERGOVERNMENTAL 
INITIATIVES 
Intergovernmental working groups consist of experts and 
policymakers from member states who study and report 
on the most urgent challenges related to developing and 
deploying AI and then make recommendations based on 
their findings. These groups are instrumental in identifying 
and developing strategies for the most pressing issues in AI 
technologies and their applications. 
Working Groups
Global Partnership on AI (GPAI)
• 
Participants: Australia, Brazil, Canada, France, Germany, 
India, Italy, Japan, Mexico, the Netherlands, New 
Zealand, South Korea, Poland, Singapore, Slovenia, 
Spain, the United Kingdom, the United States, and the 
European Union (as of December 2020)
• 
Host of Secretariat: OECD
• 
Focus Areas: Responsible AI; data governance; the future 
of work; innovation and commercialization
• 
Recent Activities: Two International Centres of 
Expertise—the International Centre of Expertise in 
Montreal for the Advancement of Artificial Intelligence 
and the French National Institute for Research in Digital 
Science and Technology (INRIA) in Paris—are supporting 
the work in the four focus areas and held the Montreal 
Summit 2020 in December 2020. Moreover, the data 
governance working group published the beta version of 
the group’s framework in November 2020.
OECD Network of Experts on AI (ONE AI)
• 
Participants: OECD countries
• 
Host: OECD
• 
Focus Areas: Classification of AI; implementing 
trustworthy AI; policies for AI; AI compute
• 
Recent Activities: ONE AI convened its first meeting in 
February 2020, when it also launched the OECD AI Policy 
Observatory. In November 2020, the working group on 
the classification of AI presented the first look at an AI 
classification framework based on OECD’s definition of AI 
divided into four dimensions (context, data and input, AI 
model, task and output) that aims to guide policymakers 
in designing adequate policies for each type of AI system.
High-Level Expert Group on Artificial Intelligence (HLEG) 
• 
Participants: EU countries
• 
Host: European Commission
• 
Focus Areas: Ethics guidelines for trustworthy AI
• 
Recent Activities: Since its launch at the recommendation 
7.2 INTERNATIONAL 
COLLABORATION ON AI
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7.2 INTERNATIONAL 
COLLABORATION 
ON AI


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of the EU AI strategy in 2018, HLEG presented the EU Ethics 
Guidelines for Trustworthy Artificial Intelligence and a 
series of policy and investment recommendations, as 
well as an assessment checklist related to the guidelines. 
Ad Hoc Expert Group (AHEG) for the Recommendation 
on the Ethics of Artificial Intelligence
• 
Participants: United Nations Educational, Scientific and 
Cultural Organization (UNESCO) member states
• 
Host: UNESCO
• 
Focus Areas: Ethical issues raised by the development 
and use of AI 
• 
Recent Activities: The AHEG produced a revised first draft 
Recommendation on the Ethics of Artificial Intelligence, 
which was transmitted in September 2020 to Member States 
of UNESCO for their comments by December 31, 2020.
Summits and Meetings
AI for Good Global Summit
• 
Participants: Global (with the United Nations and its 
agencies)
• 
Hosts: International Telecommunication Union, XPRIZE 
Foundation
• 
Focus Areas: Trusted, safe, and inclusive development of 
AI technologies and equitable access to their benefits
AI Partnership for Defense
• 
Participants: Australia, Canada, Denmark, Estonia, 
Finland, France, Israel, Japan, Norway, South Korea, 
Sweden, the United Kingdom, and the United States
• 
Hosts: Joint Artificial Intelligence Center, U.S. 
Department of Defense
• 
Focus Areas: AI ethical principles for defense
China-Association of Southeast Asian Nations (ASEAN) 
AI Summit
• 
Participants: Brunei, Cambodia, China, Indonesia, Laos, 
Malaysia, Myanmar, the Philippines, Singapore, Thailand, 
and Vietnam
• 
Hosts: China Association for Science and Technology, 
Guangxi Zhuang Autonomous Region, China
• 
Focus Areas: Infrastructure construction, digital 
economy, and innovation-driven development
BILATERAL AGREEMENTS
Bilateral agreements focusing on AI are another form 
of international collaboration that has been gaining in 
popularity in recent years. AI is usually included in the 
broader context of collaborating on the development of 
digital economies, though India stands apart for investing 
in developing multiple bilateral agreements specifically 
geared toward AI.
India and United Arab Emirates
Invest India and the UAE Ministry of Artificial Intelligence 
signed a memorandum of understanding in July 2018 
to collaborate on fostering innovative AI ecosystems 
and other policy concerns related to AI. Two countries 
will convene a working committee aimed at increasing 
investment in AI startups and research activities in 
partnership with the private sector.
India and Germany
It was reported in October 2019 that India and Germany 
likely will sign an agreement including partnerships on the 
use of artificial intelligence (especially in farming).
United States and United Kingdom
The U.S. and the U.K. announced a declaration in 
September 2020, through the Special Relationship 
Economic Working Group, that the two countries will 
enter into a bilateral dialogue on advancing AI in line with 
shared democratic values and further cooperation in AI 
R&D efforts.
India and Japan
India and Japan were said to have finalized an agreement 
in October 2020 that focuses on collaborating on digital 
technologies, including 5G and AI. 
French and Germany
France and Germany signed a road map for a Franco-
German Research and Innovation Network on artificial 
intelligence as part of the Declaration of Toulouse 
in October 2019 to advance European efforts in the 
development and application of AI, taking into account 
ethical guidelines.
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COLLABORATION 
ON AI


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2020 (Request)
2020 (Enacted)
2021 (Request)
0
500
1,000
1,500
Budget (in Millions of U.S. Dollars)
U.S. FEDERAL BUDGET for NON-DEFENSE AI R&D, FY 2020-21
Source: U.S. NITRD Program, 2020 | Chart: 2021 AI Index Report
FEDERAL BUDGET FOR  
NON-DEFENSE AI R&D 
In September 2019, the White House 
National Science and Technology Council 
released a report attempting to total 
up all public-sector AI R&D funding, the 
first time such a figure was published. 
This funding is to be disbursed as grants 
for government laboratories or research 
universities or in the form of government 
contracts. These federal budget figures, 
however, do not include substantial AI 
R&D investments by the Department of 
Defense (DOD) and the intelligence sector, 
as they were withheld from publication for 
national security reasons. 
As shown in Figure 7.3.1, federal civilian 
agencies—those agencies that are not part 
of the DOD or the intelligence sector—
allocated USD 973.5 million to AI R&D 
for FY 2020, a figure that rose to USD 1.1 
billion once congressional appropriations 
and transfers were factored in. For FY 
2021, federal civilian agencies budgeted 
USD 1.5 billion, which is almost 55% 
higher than its 2020 request. 
7.3 U.S. PUBLIC INVESTMENT IN AI
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7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
This section examines public investment in AI in the United States based on data from the U.S. Networking and Information 
Technology Research and Development (NITRD) program and Bloomberg Government. 
Figure 7.3.1
Federal civilian agencies—those 
agencies that are not part of the 
DOD or the intelligence sector—
allocated USD 973.5 million to 
AI R&D for FY 2020, a figure 
that rose to USD 1.1 billion once 
congressional appropriations 
and transfers were factored in.


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2018 (Enacted)
2019 (Enacted)
2020 (Enacted)
2021 (Request)
0
1,000
2,000
3,000
4,000
5,000
Budget (in Millions of U.S. Dollars)
927
841
DOD Reported
Budget on AI R&D
DOD Reported
Budget on AI R&D
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH DEVELOPMENT, TEST, and EVALUATION (RDT&E), FY 2018-20
Sources: Bloomberg Government & U.S. Department of Defense, 2020 | Chart: 2021 AI Index Report
Figure 7.3.2
U.S. DEPARTMENT OF DEFENSE AI 
R&D BUDGET REQUEST 
While the official DOD budget is not publicly available, 
Bloomberg Government has analyzed the department’s 
publicly available budget request for research, 
development, test, and evaluation (RDT&E)— data that 
sheds light on its spending on AI R&D. 
With 305 unclassified DOD R&D programs specifying the use 
of AI or ML technologies, the combined U.S. military budget 
for AI R&D in FY 2021 is USD 5.0 billion (Figure 7.3.2). This 
figure appears consistent with the USD 5.0 billion enacted 
the previous year. However, the FY 2021 figure reflects 
a budget request, rather than a final enacted budget. 
As noted above, once congressional appropriations are 
factored in, the true level of funding available to DOD AI R&D 
programs in FY 2021 may rise substantially.
The top five projects set to receive the highest amount of 
AI R&D investment in FY 2021: 
• 
Rapid Capability Development and Maturation, by the 
U.S. Army (USD 284.2 million)
• 
Counter WMD Technologies and Capabilities 
Development, by the DOD Threat Reduction Agency 
(USD 265.2 million) 
• 
Algorithmic Warfare Cross-Functional Team (Project 
Maven), by the Office of the Secretary of Defense (USD 
250.1 million) 
• 
Joint Artificial Intelligence Center (JAIC), by the Defense 
Information Systems Agency (USD 132.1 million)
• 
High Performance Computing Modernization Program, 
by the U.S. Army (USD 99.6 million)
In addition, the Defense Advanced Research Projects 
Agency (DARPA) alone is investing USD 568.4 million in AI 
R&D, an increase of USD 82 million from FY 2020.
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7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
Important data caveat: This chart illustrates the challenge of working with contemporary government data sources 
to understand spending on AI. By one measure—the requests that include AI-relevant keywords—the DOD is requesting 
more than USD 5 billion for AI-specific research development in 2021 . However, DOD’s own accounting produces a 
radically smaller number: USD 841 million. This relates to the issue of defining where an AI system ends and another 
system begins; for instance, an initiative that uses AI for drones may also count hardware-related expenditures for the 
drones within its “AI” budget request, though the AI software component will be much smaller.


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USD 1.5 billion agencies spent in FY 2019 (Figure 7.3.3). 
AI spending in 2020 was more than six times higher than 
what it was just five years ago—about USD 300 million in 
FY 2015. However, to put this in perspective, the federal 
government spent USD 682 billion on contracts in FY 2020, 
so AI currently represents 0.25% of government spending.
Contract Spending by Department and Agency
Figure 7.3.4 shows that in FY 2020, the DOD spent more on 
AI-related contracts than any other federal department 
or agency (USD 1.4 billion). In second and third place 
are NASA (USD 139.1 million) and the Department of 
Homeland Security (USD 112.3 million). DOD, NASA, and 
the Department of Health and Human Services top the 
list for the most contract spending on AI over the past 10 
years combined (Figure 7.3.5). In fact, DOD’s total contract 
spending on AI from 2001 to 2020 (USD 3.9 billion) is more 
than what was spent by the other 44 departments and 
agencies combined (USD 2.9 billion) over the same period.
Looking ahead, DOD spending on AI contracts is only 
expected to grow as the Pentagon’s Joint Artificial 
Intelligence Center (JAIC), established in June 2018, is 
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
500
1,000
1,500
2,000
Contract Spending (in Millions of U.S. Dollars)
1,837
U.S. GOVERNMENT TOTAL CONTRACT SPENDING on AI, FY 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Figure 7.3.3
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7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
U.S. GOVERNMENT AI-RELATED 
CONTRACT SPENDING
Another indicator of public investment in AI technologies is 
the level of spending on government contracts across the 
federal government. Contracting for products and services 
supplied by private businesses typically occupies the largest 
share of an agency’s budget. Bloomberg Government built 
a model that captures contract spending on AI technologies 
by adding up all contracting transactions that contain a 
set of more than 100 AI-specific keywords in their titles or 
descriptions. The data reveals that the amount the federal 
government spends on contracts for AI products and 
services has reached an all-time high and shows no sign of 
slowing down. However, note that during the procurement 
process, vendors may add a bunch of keywords into their 
applications, so some of these things may have a relatively 
small AI component relative to other parts of technology.
Total Contract Spending
Federal departments and agencies spent a combined 
USD 1.8 billion on unclassified AI-related contracts in FY 
2020. This represents a more than 25% increase from the 


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0
500
1000
1500
2000
2500
3000
3500
4000
Contract Spending (in Millions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and
Space Administration (NASA)
Department of Health and
Human Services (HHS)
Department of the Treasury
(TREAS)
Department of Homeland
Security (DHS)
Department of Veterans A airs
(VA)
Department of Commerce
(DOC)
Department of Agriculture
(USDA)
General Services
Administration (GSA)
Department of State (DOS)
TOP 10 CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2001-20 (SUM)
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
0
200
400
600
800
1,000
1,200
1,400
Contract Spending (in Millions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and Space
Administration (NASA)
Department of Homeland Security
(DHS)
Department of Health and Human
Services (HHS)
Department of Commerce (DOC)
Department of the Treasury
(TREAS)
Department of Veterans A airs
(VA)
Securities and Exchange
Commission (SEC)
Department of Agriculture (USDA)
Department of Justice (DOJ)
TOP 10 CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2020
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Figure 7.3.4
Figure 7.3.5
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7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
still in the early stages of driving DOD’s AI spending. In 
2020, JAIC awarded two massive contracts, one to Booz 
Allen Hamilton for the five-year, USD 800 million Joint 
Warfighter program, and another to Deloitte Consulting for 
a four-year, USD 106 million enterprise cloud environment 
for the JAIC, known as the Joint Common Foundation.


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107Th
(2001-2002)
108Th
(2003-2004)
109Th
(2005-2006)
110Th
(2007-2008)
111Th
(2009-2010)
112Th
(2011-2012)
113Th
(2013-2014)
114Th
(2015-2016)
115Th
(2017-2018)
116th
(2019-2020)
0
100
200
300
400
500
Number of Mentions
486
149
22
10
16
15
17
4
8
7
243
173
44
66
39
70
MENTIONS of AI in U.S. CONGRESSIONAL RECORD by LEGISLATIVE SESSION, 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Congressional Research Service Reports
Committee Reports
Legislation
As AI gains attention and importance, policies and 
initiatives related to the technology are becoming higher 
priorities for governments, private companies, technical 
organizations, and civil society. This section examines 
how three of these four are setting the agenda for AI 
policymaking, including the legislative and monetary 
authority of national governments, as well as think tanks, 
civil society, and the technology and consultancy industry. 
LEGISLATION RECORDS ON AI
The number of congressional and parliamentary 
records on AI is an indicator of governmental interest 
in developing AI capabilities—and legislating issues 
pertaining to AI. In this section, we use data from 
Bloomberg and McKinsey & Company to ascertain the 
7.4 AI AND POLICYMAKING
CHAPTER 7:
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NATIONAL STRATEGIES
7.4 AI AND 
POLICYMAKING
Figure 7.4.1
number of these records and how that number has 
evolved in the last 10 years. 
Bloomberg Government identified all legislation (passed 
or introduced), reports published by congressional 
committees, and CRS reports that referenced one or more 
AI-specific keywords. McKinsey & Company searched for 
the terms “artificial intelligence” and “machine learning” 
on the websites of the U.S. Congressional Record, the U.K. 
Parliament, and the Parliament of Canada. For the United 
States, each count indicates that AI or ML was mentioned 
during a particular event contained in the Congressional 
Record, including the reading of a bill; for the U.K. and 
Canada, each count indicates that AI or ML was mentioned 
in a particular comment or remark during the proceedings.1
1 If a speaker or member mentioned artificial intelligence (AI) or machine learning (ML) multiple times within remarks, or multiple speakers mentioned AI or ML within the same event, it appears only 
once as a result. Counts for AI and ML are separate, as they were conducted in separate searches. Mentions of the abbreviations “AI” or “ML” are not included.


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2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
20
40
60
80
100
120
140
Number of Mentions
120
129
92
27
0
9
8
1
1
1
101
92
28
28
25
23
67
6
7
MENTIONS of AI and ML in the PROCEEDINGS of U.S. CONGRESS, 2011-20
Sources: U.S. Congressional Record website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
U.S. Congressional Record
The 116th Congress (January 1, 2019–January 3, 2021) is 
the most AI-focused congressional session in history. The 
number of mentions of AI by this Congress in legislation, 
committee reports, and CRS reports is more than triple 
that of the 115th Congress. Congressional interest in AI 
has continued to accelerate in 2020. Figure 7.4.1 shows 
that during this congressional session, 173 distinct 
pieces of legislation either focused on or contained 
language about AI technologies, their development, 
use, and rules governing them. During that two-year 
period, various House and Senate committees and 
CHAPTER 7:
AI POLICY AND  
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7.4 AI AND 
POLICYMAKING
Figure 7.4.2
subcommittees commissioned 70 reports on AI, while 
the CRS, tasked as a fact-finding body for members of 
Congress, published 243 about AI or referencing AI. 
Mentions of AI and ML in Congressional/
Parliamentary Proceedings
As shown in Figures 7.4.2–7.4.5, the number of mentions 
of artificial intelligence and machine learning in the 
proceedings of the U.S. Congress and the U.K. parliament 
continued to rise in 2020, while there were fewer 
mentions in the parliamentary proceedings of Canada. 


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2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
50
100
150
200
250
300
Number of Mentions
283
192
183
138
51
0
4
5
7
1
246
158
138
179
34
42
37
MENTIONS of AI and ML in the PROCEEDINGS of U.K. PARLIAMENT, 2011-20
Sources: Parliament of U.K. website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
10
20
30
40
Number of Mentions
34
38
18
21
0
0
0
0
0
2
35
33
21
17
3
MENTIONS of AI and ML in the PROCEEDINGS of CANADIAN PARLIAMENT, 2011-20
Sources: Canadian Parliament website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
CHAPTER 7:
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7.4 AI AND 
POLICYMAKING
Figure 7.4.3
Figure 7.4.4


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7.4 AI AND 
POLICYMAKING
2 See Science & Technology Review and Scientific American for more details.
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
200
400
600
800
1,000
Number of Mentions
225
MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD, 2011-20
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report
Figure 7.4.5
CENTRAL BANKS
Central banks play a key role in conducting currency and 
monetary policy in a country or a monetary union. As 
with many other institutions, central banks are tasked 
with integrating AI into their operations and relying on 
big data analytics to assist them with forecasting, risk 
management, and financial supervision. 
Prattle, a leading provider of automated investment 
research solutions, monitors mentions of AI in the 
communications of central banks, including meeting 
minutes, monetary policy papers, press releases, 
speeches, and other official publications. 
Figure 7.4.5 shows a significant increase in the mention 
of AI across 16 central banks over the past 10 years, 
with the number reaching a peak of 1,020 in 2019. The 
sharp decline in 2020 can be explained by the COVID-19 
pandemic as most central bank communications focused 
on responses to the economic downturn. Moreover, 
the Federal Reserve in the United States, Norges Bank 
in Norway, and the European Central Bank top the 
list for the most aggregated number of AI mentions in 
communications in the past five years (Figure 7.4.6). 


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0
200
400
600
800
1,000
1,200
1,400
1,600
1,800 2,000
Number of Mentions
Federal Reserve
Norges Bank
European Central Bank
Reserve Bank of India
Bank of England
Bank of Israel
Bank of Japan
Bank of Korea
Reserve Bank of Australia
Reserve bank of New Zealand
Bank of Taiwan
Bank of Canada
Sveriges Riksbank
Swedish Riksbank
Central Bank of the Republic of Turkey
Central Bank of Brazil
MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD by BANK, 2016-20 (SUM)
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report
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7.4 AI AND 
POLICYMAKING
Figure 7.4.6


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0
20
40
60
80
100
120
140
160
Number of Policy Products
Innovation & Technology
Int'l Affairs & Int'l Security
Industry & Regulation
Workforce & Labor
Government & Public Administration
Privacy, Safety & Security
Ethics
Justice & Law Enforcement
Equity & Inclusion
Education & Skills
Social & Behavioral Sciences
Health & Biological Sciences
Communications & Media
Democracy
Humanities
Energy & Environment
Physical Sciences
U.S. AI POLICY PRODUCTS by TOPIC, 2019-20 (SUM)
Source: Stanford HAI & AI Index, 2020 | Chart: 2021 AI Index Report
Secondary Topic
Primary Topic
U.S. AI POLICY PAPERS
What are the AI policy initiatives outside national and 
intergovernmental governments? We monitored 42 
prominent organizations that deliver policy papers on 
topics related to AI and assessed the primary topic as 
well as the secondary topic on policy papers published 
in 2019 and 2020. (See the Appendix for a complete list 
of organizations included.) Those organizations are 
either U.S.-based or have a sizable presence in the United 
States, and we grouped them into three categories: think 
tanks, policy institutes and academia (27); civil society 
organizations, associations and consortiums (9); and 
industry and consultancy (6). 
AI policy papers are defined as research papers, research 
reports, blog posts, and briefs that focus on a specific policy 
issue related to AI and provide clear recommendations 
CHAPTER 7:
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7.4 AI AND 
POLICYMAKING
Figure 7.4.7
for policymakers. Primary topics mean that such a topic is 
the main focus of the policy paper, while secondary topics 
mean that the policy paper either briefly touches on the 
topic or the topic is a sub-focus of the paper. 
Combined data for 2019 and 2020 suggests that the topics 
of innovation and technology, international affairs and 
international security, and industry and regulation are 
the main focuses of AI policy papers in the United States 
(Figure 7.4.7). Fewer documents placed a primary focus 
on topics related to AI ethics—such as ethics, equity and 
inclusion; privacy, safety and security; and justice and law 
enforcement—which have largely been secondary topics. 
Moreover, topics bearing on the physical sciences, energy 
and environment, humanities, and democracy have 
received the least attention in U.S. AI policy papers. 


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APPENDIX
CHAPTER 7:  
AI POLICY AND  
NATIONAL STRATEGIES
APPENDIX
BLOOMBERG GOVERNMENT 
Bloomberg Government (BGOV) is a subscription-
based market intelligence service designed to make 
U.S. government budget and contracting data more 
accessible to business development and government 
affairs professionals. BGOV’s proprietary tools ingest 
and organize semi-structured government data sets 
and documents, enabling users to track and forecast 
investment in key markets. 
Methodology
The BGOV data included in this section was drawn from 
three original sources:
Contract Spending: BGOV’s Contracts Intelligence Tool 
ingests on a twice-daily basis all contract spending data 
published to the beta.SAM.gov Data Bank, and structures 
the data to ensure a consistent picture of government 
spending over time. For the section “U.S. Government 
Contract Spending,” BGOV analysts used FPDS-NG data, 
organized by the Contracts Intelligence Tool, to build a 
model of government spending on artificial intelligence-
related contracts in the fiscal years 2000 through 2021. 
BGOV’s model used a combination of government-
defined produce service codes and more than 100 
AI-related keywords and acronyms to identify AI-related 
contract spending.
Defense RDT&E Budget: BGOV organized all 7,057 
budget line items included in the RDT&E budget request 
based on data available on the DOD Comptroller website. 
For the section “U.S. Department of Defense (DOD) 
Budget,” BGOV used a set of more than a dozen AI-
specific keywords to identify 305 unique budget activities 
related to artificial intelligence and machine learning 
worth a combined USD 5.0 billion in FY 2021.
Congressional Record (available on Congressional 
Record website): BGOV maintains a repository of 
congressional documents, including bills, amendments, 
bill summaries, Congressional Budget Office 
assessments, reports published by congressional 
committees, Congressional Research Service (CRS), and 
others. For the section “U.S. Congressional Record,” 
BGOV analysts identified all legislation (passed or 
introduced), congressional committee reports, and 
CRS reports that referenced one or more of a dozen AI-
specific keywords. Results are organized by a two-year 
congressional session.
LIQUIDNET
Prepared by Jeffrey Banner and Steven Nichols
Source
Liquidnet provides sentiment data that predicts 
the market impact of central bank and corporate 
communications. Learn more about Liquidnet here. 
Examples of Central Bank Mentions
Here are some examples of how AI is mentioned by 
central banks: In the first case, China uses a geopolitical 
environment simulation and prediction platform 
that works by crunching huge amounts of data and 
then providing foreign policy suggestions to Chinese 
diplomats or the Bank of Japan use of AI prediction 
models for foreign exchange rates. For the second 
case, many central banks are leading communications 
through either official documents—for example, on 
July 25, 2019, the Dutch Central Bank (DNB) published 
Guidelines for the use of AI in financial services and 
launched its six “SAFEST” principles for regulated firms 
to use AI responsibly—or a speech on June 4, 2019, by 
the Bank of England’s Executive Director of U.K. Deposit 
Takers Supervision James Proudman, titled “Managing 
Machines: The Governance of Artificial Intelligence,” 
focused on the increasingly important strategic issue of 
how boards of regulated financial services should use AI. 


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APPENDIX
MCKINSEY GLOBAL INSTITUTE
Source
Data collection and analysis was performed by the 
McKinsey Global Institute (MGI).
Canada (House of Commons)
Data was collected using the Hansard search feature on 
Parliament of Canada website. MGI searched for the terms 
“Artificial Intelligence” and “Machine Learning” (quotes 
included) and downloaded the results as a CSV. The date 
range was set to “all debates.” Data is as of Dec. 31, 2020. 
Data are available online from Aug. 31, 2002.
Each count indicates that Artificial Intelligence or Machine 
Learning was mentioned in a particular comment or remark 
during the proceedings of the House of Commons. This 
means that within an event or conversation, if a member 
mentions AI or ML multiple times within their remarks, it 
will appear only once. However if, during the same event, 
the speaker mentions AI or ML in separate comments (with 
other speakers in between), it will appear multiple times. 
Counts for Artificial Intelligence or Machine Learning are 
separate, as they were conducted in separate searches. 
Mentions of the abbreviations AI or ML are not included. 
United Kingdom (House of Commons, House of 
Lords, Westminster Hall, and Committees)
Data was collected using the Find References feature of the 
Hansard website of the U.K. Parliament. MGI searched for 
the terms “Artificial Intelligence” and “Machine Learning” 
(quotes included) and catalogued the results. Data is as 
of Dec. 31, 2020. Data are available online from January 1, 
1800 onward. Contains Parliamentary information licensed 
under the Open Parliament Licence v3.0.
As in Canada, each count indicates that Artificial 
Intelligence or Machine Learning was mentioned in a 
particular comment or remark during a proceeding. 
Therefore, if a member mentions AI or ML multiple times 
within their remarks, it will appear only once. However 
if, during the same event, the same speaker mentions 
AI or ML in separate comments (with other speakers in 
between), it will appear multiple times. Counts for Artificial 
Intelligence or Machine Learning are separate, as they 
were conducted in separate searches. Mentions of the 
abbreviations AI or ML are not included. 
United States (Senate and House of 
Representatives)
Data was collected using the advanced search feature 
of the U.S. Congressional Record website. MGI searched 
the terms “Artificial Intelligence” and “Machine Learning” 
(quotes included) and downloaded the results as a 
CSV. The “word variant” option was not selected, and 
proceedings included Senate, House of Representatives, 
and Extensions of Remarks, but did not include the Daily 
Digest. Data is as of Dec. 31, 2020, and data is available 
online from the 104th Congress onward (1995).
Each count indicates that Artificial Intelligence or Machine 
Learning was mentioned during a particular event 
contained in the Congressional Record, including the 
reading of a bill. If a speaker mentioned AI or ML multiple 
times within remarks, or multiple speakers mentioned AI or 
ML within the same event, it would appear only once as a 
result. Counts for Artificial Intelligence or Machine Learning 
are separate, as they were conducted in separate searches. 
Mentions of the abbreviations AI or ML are not included.
U.S. AI POLICY PAPER
Source
Data collection and analysis was performed by Stanford 
Institute of Human-Centered Artificial Intelligence and AI Index.
Organizations
To develop a more nuanced understanding of the 
thought leadership that motivates AI policy, we tracked 
policy papers published by 36 organizations across three 
broad categories including: 
Think Tanks, Policy Institutes & Academia: This includes 
organizations where experts (often from academia and 
the political sphere) provide information and advice 
on specific policy problems. We included the following 
27 organizations: AI PULSE at UCLA Law, American 
Enterprise Institute, Aspen Institute, Atlantic Council, 
Berkeley Center for Long-Term Cybersecurity, Brookings 


29
Artificial Intelligence
Index Report 2021
CHAPTER 7 PREVIEW
CHAPTER 7:  
AI POLICY AND  
NATIONAL STRATEGIES
APPENDIX
Institution, Carnegie Endowment for International Peace, 
Cato Institute, Center for a New American Security, 
Center for Strategic and International Studies, Council 
on Foreign Relations, Georgetown Center for Security 
and Emerging Technology (CSET), Harvard Belfer Center, 
Harvard Berkman Klein Center, Heritage Foundation, 
Hudson Institute, MacroPolo, MIT Internet Policy Research 
Initiative, New America Foundation, NYU AI Now Institute, 
Princeton School of Public and International Affairs, RAND 
Corporation, Rockefeller Foundation, Stanford Institute 
for Human-Centered Artificial Intelligence (HAI), Stimson 
Center, Urban Institute, Wilson Center.
Civil Society, Associations & Consortiums: Not-for profit 
institutions including community-based organizations 
and NGOs advocating for a range of societal issues. We 
included the following nine organizations: Algorithmic 
Justice League, Alliance for Artificial Intelligence in 
Healthcare, Amnesty International, EFF, Future of Privacy 
Forum, Human Rights Watch, IJIS, Institute for Electrical 
and Electronics Engineers, Partnership on AI
Industry & Consultancy: Professional practices providing 
expert advice to clients and large industry players. We 
included six prominent organizations in this space: Accenture, 
Bain & Co., BCG, Deloitte, Google AI, McKinsey & Company
Methodology
Each broad topic area is based on a collection of underlying 
keywords that describes the content of the specific paper. 
We included 17 topics that represented the majority of 
discourse related to AI between 2019-2020. These topic 
areas and the associated keywords are listed below.
• 
Health & Biological Sciences: medicine, healthcare 
systems, drug discovery, care, biomedical research, 
insurance, health behaviors, COVID-19, global health
• 
Physical Sciences: chemistry, physics, astronomy, earth 
science
• 
Energy & Environment: Energy costs, climate change, 
energy markets, pollution, conservation, oil & gas, 
alternative energy
• 
International Affairs & International Security: 
international relations, international trade, developing 
countries, humanitarian assistance, warfare, regional 
security, national security, autonomous weapons
• 
Justice & Law Enforcement: civil justice, criminal justice, 
social justice, police, public safety, courts
• 
Communications & Media: social media, disinformation, 
media markets, deepfakes
• 
Government & Public Administration: federal 
government, state government, local government, public 
sector efficiency, public sector effectiveness, government 
services, government benefits, government programs, 
public works, public transportation
• 
Democracy: elections, rights, freedoms, liberties, 
personal freedoms
• 
Industry & Regulation: economy, antitrust, M&A, 
competition, finance, management, supply chain, 
telecom, economic regulation, technical standards, 
autonomous vehicle industry & regulation
• 
Innovation & Technology: advancements and 
improvements in AI technology, R&D, intellectual 
property, patents, entrepreneurship, innovation 
ecosystems, startups, computer science, engineering
• 
Education & Skills: early childhood, K-12, higher 
education, STEM, schools, classrooms, reskilling
• 
Workforce & Labor: labor supply and demand, talent, 
immigration, migration, personnel economics, future of 
work
• 
Social & Behavioral Sciences: sociology, linguistics, 
anthropology, ethnic studies, demography, geography, 
psychology, cognitive science
• 
Humanities: arts, music, literature, language, 
performance, theater, classics, history, philosophy, 
religion, cultural studies
• 
Equity & Inclusion: biases, discrimination, gender, 
race, socioeconomic inequality, disabilities, vulnerable 
populations
• 
Privacy, Safety & Security: anonymity, GDPR, 
consumer protection, physical safety, human control, 
cybersecurity, encryption, hacking
• 
Ethics: transparency, accountability, human 
values, human rights, sustainability, explainability, 
interpretability, decision-making norms


Artificial Intelligence
Index Report 2022
CHAPTER 5: 
AI Policy and  
Governance


2
Artificial Intelligence
Index Report 2022
Overview	
  3
Chapter Highlights	
  4
5.1 AI AND POLICYMAKING	
  5
Global Legislation Records on AI	
  5
	
By Geographic Area	
  6
	
Federal AI Legislation in the 
	
United States	
  7
	
Highlight: A Closer Look 
	
at the Legislation	
  8
State-Level AI Legislation 
in the United States	
  9
	
By State	
 10
	
Sponsorship by Political Party	
 11
Mentions of AI in Legislative Records	
 12
	
AI Mentions in U.S. Congressional 
	
Records	
 12
	
AI Mentions in Global Legislative 
	
Proceedings	
 13
	
By Geographic Area	
 14
U.S. AI Policy Papers	
 15
	
By Topic	
 16
5.2 U.S. PUBLIC INVESTMENT IN AI	
 17 
Federal Budget for Nondefense AI R&D	
 17
U.S. Department of Defense 
Budget Request	
 18
	
Highlight: DOD Top Five 
	
Highest-Funded Programs	
 19
	
DOD AI R&D Spending by Department	  20
U.S. Government AI-Related  
Contract Spending	
 21
	
Total Contract Spending	
 21
	
Contract Spending by Department 
	
and Agency	
 22
	
Highlight: Largest Contract for Five
	
Top-Spending Departments in 2021	
 24
APPENDIX	
 25 
Chapter Preview
CHAPTER 5:
ACCESS THE PUBLIC DATA
CHAPTER 5: AI POLICY AND GOVERNANCE


3
Chapter 5 Preview
Artificial Intelligence
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Overview
As AI has become an increasingly ubiquitous topic in the last decade, 
intergovernmental, national, and regional organizations have worked to 
develop policies and strategies around AI governance. These actors are 
driven by the understanding that it is imperative to find ways to address 
the ethical and societal concerns surrounding AI, while maximizing 
its benefits. Active and informed governance of AI technologies has 
become a priority for many governments around the world.
This chapter examines the intersection of AI and governance, and takes 
a closer look at how governments in different countries, regions, and 
U.S. states are working to manage AI technologies. It begins by looking 
at AI policymaking across the globe and within the United States, 
exploring which countries and political actors are most keen to advance 
AI legislation, and what kind of AI subtopics, from privacy to ethics, are 
the focus of most legislative attention. Then the chapter takes a deep 
dive into one of the world’s top public sector investors in AI, the United 
States, and studies how much its various government departments have 
spent on AI in the past five years.
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
CHAPTER HIGHLIGHTS
• 
An AI Index analysis of legislative records on AI in 25 countries shows that the number of bills 
containing “artificial intelligence” that were passed into law grew from just 1 in 2016 to 18 in 
2021. Spain, the United Kingdom, and the United States passed the highest number of AI-related 
bills in 2021, with each adopting three.
• 
The federal legislative record in the United States shows a sharp increase in the total number of 
proposed bills that relate to AI from 2015 to 2021, while the number of bills passed remains low, 
with only 2% ultimately becoming law.
• 
State legislators in the United States passed 1 out of every 50 proposed bills that contain AI 
provisions in 2021, while the number of such bills proposed grew from 2 in 2012 to 131 in 2021.
• 
In the United States, the current congressional session (the 117th) is on track to record the greatest 
number of AI-related mentions since 2001, with 295 mentions by the end of 2021, half way 
through the session, compared to 506 in the previous (116th) session.


5
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GLOBAL LEGISLATION  
RECORDS ON AI 
​
Governments and legislative bodies across the globe are 
increasingly seeking to pass laws to provide funding for 
AI development and innovation, while also promoting the 
integration of human-centered values. The AI Index has 
conducted an analysis of laws passed in 25 countries by 
their legislative bodies that contain the words “artificial 
intelligence” from 2016 to 2021.
Taken together, the 25 countries analyzed have passed a 
total of 55 AI-related bills. Figure 5.2.1 demonstrates that in 
the past six years, there has been a sharp increase in terms 
of the total number of AI-related bills passed into law.1
5.1 AI AND POLICYMAKING
1 Note that the analysis only includes laws passed by national legislative bodies (e.g.  congress, parliament) with the keyword “artificial intelligence” in various languages in the title or body of the bill 
text. See the appendix for the methodology. Countries included: Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, the Netherlands, New Zealand, 
Norway, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, the United Kingdom, and the United States.
Artificial Intelligence
Index Report 2022
Discussions around AI governance regulation have accelerated over the past decade, resulting in policy proposals across various 
legislative bodies. This section first examines AI-related legislation that has either been proposed or passed into law across different 
countries and regions, followed by a focused analysis of state-level legislation in the United States. It then takes a closer look at 
congressional and parliamentary records on AI across the world and concludes with data on the number of policy papers published 
in the United States.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2016
2017
2018
2019
2020
2021
0
5
10
15
Number of AI-Related Bills
18
NUMBER of AI-RELATED BILLS PASSED into LAW in 25 SELECT COUNTRIES, 2016–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.1


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Artificial Intelligence
Index Report 2022
By Geographic Area
Figure 5.1.2a shows the number of laws containing 
mentions of AI that were enacted in 2021. Spain, the 
United Kingdom, and the United States led, each passing 
three. Figure 5.1.2b shows the total number of legislation 
passed in the past six years. The United States dominated 
the list with 13 bills, starting in 2017 with 3 new laws 
passed each subsequent year, followed by Russia, 
Belgium, Spain, and the United Kingdom.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
0
1
2
3
4
Number of AI-Related Bills
Spain
United Kingdom
United States
Belgium
Russia
France
Germany
Italy
Japan
South Korea
3
3
3
2
2
1
1
1
1
1
NUMBER of AI-RELATED BILLS PASSED into LAW in SELECT COUNTRIES, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.2a
The United States 
dominated the list with 13 
bills, starting in 2017 with 
3 new laws passed each 
subsequent year, followed 
by Russia, Belgium, Spain, 
and the United Kingdom.


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Federal AI Legislation in the United States
A closer look at the federal legislative record in the 
United States shows a sharp increase in the total number 
of proposed bills that relate to AI (Figure 5.1.3). In 2015, 
just one federal bill was proposed, while in 2021, there 
were 130. Although this jump is significant, the number 
of bills related to AI being passed has not kept pace with 
the growing volume of proposed AI-related bills. This gap 
was most evident in 2021, when only 2% of all federal-
level AI-related bills were ultimately passed into law. 
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2015
2016
2017
2018
2019
2020
2021
0
20
40
60
80
100
120
Number of AI-Related Bills
130, Proposed
3, Passed
NUMBER of AI-RELATED BILLS in the UNITED STATES, 2015–21 (PROPOSED vs. PASSED)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.3
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Number of AI-Related Bills
United States
Russia
Belgium
Spain
United Kingdom
France
Italy
South Korea
Japan
China
Brazil
Canada
Germany
India
13
6
4
4
4
3
5
5
5
2
1
1
1
1
NUMBER of AI-RELATED BILLS PASSED into LAW in SELECT COUNTRIES, 2016–21 (SUM)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.2b


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5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
A Closer Look at the Legislation
The following subsection delves into some of the AI-related legislation passed into law since 2016. 
Table 5.1.1 demonstrates the wide range of AI-related issues that have piqued policymakers’ interest.
Country
Year Passed
Bill Name
Description
Canada
2017
Budget Implementation Act 2017, No. 1
A provision of this act authorized the Canadian 
government to make a payment of $125 million 
to the Canadian Institute for Advanced Research 
to support the development of a pan-Canadian 
artificial intelligence strategy. 
China
2019
Law of the People’s Republic of China 
on Basic Medical and Health Care and 
the Promotion of Health
A provision of this law aimed to promote the 
application and development of big data and 
artificial intelligence in the health and medical field 
while accelerating the construction of medical and 
healthcare information infrastructure, developing 
technical standards on the collection, storage, 
analysis, and application of medical and health data.
Russia
2020
Federal Law of 24 April 2020 No. 
123-FZ on the Experiment to Establish 
Special Regulation in order to Create 
the Necessary Conditions for the 
Development and Implementation of 
Artificial Intelligence Technologies in 
the Region of the Russian Federation 
– Federal City of Moscow and 
Amending the Articles 6 and 10 of the 
Federal Law on Personal Data
This law established an experimental framework 
for the development and implementation of AI as 
a five-year experiment to start in Moscow in July 
1, 2020, including allowing AI systems to process 
anonymized personal data for governmental and 
certain commercial business activities.
United Kingdom
2020
Supply and Appropriation (Main 
Estimates) Act 2020, c.13
A provision of this act authorized the Office of 
Qualifications and Examination Regulation to 
explore opportunities for using artificial intelligence 
to improve the marking and administration of high-
stakes qualifications.
United States
2020
IOGAN ACT: Identifying Outputs of 
Generative Adversarial Networks Act
This act directed the National Science Foundation 
to support research dedicated to studying the 
outputs of generative adversarial networks 
(deepfakes) and other comparable technologies.
Belgium
2021
Decree on coaching and solution-
oriented support for job seekers, N. 
327
A provision of this act directs the government 
to create an advisory group called the Ethics 
Committee, which is responsible for submitting 
advice if artificial intelligence tools are to be used 
for digitization activities.
France
2021
Law N:2021-1485 of November 
15, 2021, aimed at reducing the 
environmental footprint of digital 
technology in France
This act sets up a monitoring system to evaluate 
environmental impacts of newly emerging digital 
technologies, in particular, artificial intelligence. 
Table 5.1.1


9
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Artificial Intelligence
Index Report 2022
STATE-LEVEL AI LEGISLATION IN 
THE UNITED STATES
Growing policy interest in AI can also be seen in the 
large number of AI-related bills recently proposed 
at the state level in the United States, based on data 
provided by Bloomberg Government since 2012. 
Bloomberg Government classified a bill as relating to 
AI if it contained AI-related keywords such as artificial 
intelligence, machine learning, or algorithmic bias.
As is the case on the federal level, there has been a 
significant increase in the number of AI bills proposed 
at the state level in the last decade (Figure 5.1.4). 
In 2012, the first two pieces of AI-related legislation 
were proposed when New Jersey assembly member 
Annette Quijano directed the New Jersey Motor Vehicle 
Commission to establish driver’s license endorsements 
for autonomous vehicles. In the past 10 years, the 
increase has been substantial, from 2 bills in 2012 to 131 
in 2021.
A notable difference between AI-related lawmaking in the 
United States on the federal versus the state level is that 
a greater proportion of proposed state-level AI bills have 
actually passed. In 2021, of the 131 proposed state bills, 
26 were passed into law (20%), or 1 out of 5 proposed 
bills became law. This ratio is significantly higher when 
compared to the federal level, where 1 out of every 50 
proposed bills became law in 2021.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0
20
40
60
80
100
120
140
Number of AI-Related Bills
103
66
26
25
74
10
13
12
17
9
8
2
10
14
9
9
29
26
87
77
131
NUMBER of STATE-LEVEL AI-RELATED BILLS in the UNITED STATES, 2012–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.4
Passed
Proposed
Vetoed
A notable difference between 
AI-related lawmaking in the 
United States on the federal 
versus the state level is 
that a greater proportion of 
proposed state-level AI bills 
have actually passed.


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Artificial Intelligence
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By State
In the United States, AI 
lawmaking has been relatively 
widespread across all states. As 
of 2021, 41 out of 50 states have 
proposed at least one AI-related 
bill, but certain states have been 
particularly active in generating 
AI legislation. Figure 5.1.5 shows 
that Massachusetts has proposed 
the most AI bills, with 40 since 
2012, followed by Hawaii (35) 
and New Jersey (32). Focusing 
on just 2021 in Figure 5.1.6, 
Massachusetts was the state that 
proposed the most AI-related 
bills, with 20, followed by Illinois 
(15) and Alabama (12).
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
MO
4
NM
1
MN
2
WA
14
MD
8
WV
9
MA
40
WY
0
CO
2
OH
3
MS
7
MT
0
ME
0
NC
6
NH
0
ND
0
OK
2
DC
8
GA
3
CA
29
OR
1
NV
10
NY
31
AK
0
TN
7
VA
8
NE
2
SC
1
CT
2
AZ
7
AR
1
SD
0
DE
1
NJ
32
PA
7
KY
1
WI
0
UT
3
KS
1
VT
8
LA
0
AL
21
MI
3
TX
17
HI
35
FL
22
IL
28
IN
1
ID
0
IA
1
RI
5
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED
STATES by STATE, 2012–21 (SUM)
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.5
MO
1
NM
0
MN
0
WA
6
MD
4
WV
3
MA
20
WY
0
CO
2
OH
1
MS
3
MT
0
ME
0
NC
3
NH
0
ND
0
OK
1
DC
6
GA
0
CA
4
OR
1
NV
1
NY
8
AK
0
TN
2
VA
1
NE
0
SC
1
CT
0
AZ
0
AR
0
SD
0
DE
0
NJ
4
PA
3
KY
0
WI
0
KS
0
UT
2
VT
3
LA
0
AL
12
MI
0
TX
6
FL
7
IN
0
HI
7
ID
0
IA
1
IL
15
RI
3
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED
STATES by STATE, 2021
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.6


11
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Sponsorship by Political Party
State-level AI legislation data reveals that there is a 
partisan dynamic to AI lawmaking. Figure 5.1.7 plots the 
number of AI-related bills sponsored at the state level by 
Democratic and Republican lawmakers. Although there 
has been an increase in AI bills proposed by members 
of both parties since 2012, in the past four years, the 
data suggests Democrats were more likely to sponsor 
AI-related legislation. Whereas Democrats sponsored 
only two more AI bills than Republicans in 2018, they 
sponsored 39 more in 2021.
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0
10
20
30
40
50
60
70
80
Number of AI-Related Bills
79, Democratic
40, Republican
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED STATES by SPONSOR PARTY, 2012–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.7
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE


12
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MENTIONS OF AI IN LEGISLATIVE 
RECORDS
Another barometer of legislative interest in AI is the 
number of mentions of “artificial intelligence” in 
governmental and parliamentary proceedings. This 
subsection considers data on mentions of AI both 
in U.S. congressional records and the parliamentary 
proceedings of other countries based on AI Index and 
Bloomberg Government data. 
AI Mentions in U.S. Congressional Records
In the last five years, and especially in 2021, U.S. 
congressional sessions have devoted increasing amounts 
of time to discussions of AI. This section presents data 
from Bloomberg Government concerning mentions of AI-
related keywords in congressional proceedings, broken 
down by legislation, congressional committee reports, 
and congressional research service reports.
According to Figure 5.1.8, the current congressional 
session (the 117th) is on track (as of the end of 2021) 
to record the greatest number of AI-related mentions 
since 2001. The most recently completed congressional 
session, the 116th (2019-2020), saw 506 AI mentions, 
nearly 3.4 times as many mentions as there were during 
the 115th session (2017–2018), and 30 times as many as 
the 114th session (2015–2016).
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
107th
(2001–02)
108th
(2003–04)
109th
(2005–06)
110th
(2007–08)
111th
(2009–10)
112th
(2011–12)
113th
(2013–14)
114th
(2015–16)
115th
(2017–18)
116th
(2019–20)
117th
(2021–)
0
100
200
300
400
500
Number of Mentions
245
139
129
178
66
44
39
83
27
4
7
25
17
18
17
12
17
149
506
295
MENTIONS of AI in the U.S. CONGRESSIONAL RECORD by LEGISLATIVE SESSION, 2001–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.8
Legislation
Congressional Research Service Reports
Committee Reports


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AI Mentions in Global Legislative Proceedings
AI mentions in governmental proceedings are on the 
rise not only in the United States but also in many other 
countries across the world. The AI Index conducted an 
analysis on the minutes or proceedings of legislative 
sessions in 25 countries that contain the keyword 
“artificial intelligence” from 2016 to 2021. Figure 5.1.9 
shows that the mentions of AI in legislative proceedings 
in 25 select countries grew 7.7 times in the past six years.2
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2016
2017
2018
2019
2020
2021
0
200
400
600
800
1,000
1,200
Number of Mentions
1,323
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in 25 SELECT COUNTRIES, 2016–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.9
2 See the appendix for the methodology. Countries included: Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, the Netherlands, New Zealand, 
Norway, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, the United Kingdom, and the United States.


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By Geographic Area
Figure 5.1.10a shows the number of legislative 
proceedings containing mentions of AI that were 
enacted in 2021. Similar to the trend in the number of 
AI mentions in bills passed into laws, Spain, the United 
Kingdom, and the United States topped the list. Figure 
5.1.2b shows the total number of AI mentions in the past 
six years. The United Kingdom dominated the list with 
939 mentions, followed by Spain, Japan, the United 
States, and Australia.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
0
50
100
150
200
250
300
Number of Mentions
Spain
United Kingdom
United States
Australia
Japan
Ireland
Brazil
Italy
Singapore
Belgium
Germany
France
Canada
Norway
Sweden
Finland
Russia
South Africa
Netherlands
India
New Zealand
South Korea
Denmark
Switzerland
269
185
132
122
60
46
64
20
95
25
76
47
22
72
10
16
12
12
11
6
6
3
5
7
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in
SELECT COUNTRIES, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.10a
0
200
400
600
800
1000
Number of Mentions
United Kingdom
Spain
Japan
United States
Australia
Singapore
Ireland
Italy
Germany
France
Brazil
Belgium
Canada
Finland
Sweden
Netherlands
Russia
Norway
India
South Africa
Denmark
New Zealand
South Korea
Switzerland
466
939
559
422
282
222
410
164
120
158
155
123
34
58
33
52
67
111
78
27
27
15
21
71
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in
SELECT COUNTRIES, 2016–2021 (SUM)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.10b


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U.S. AI POLICY PAPERS
To estimate activities outside national governments 
that are also informing AI-related rulemaking, the 
AI Index tracks 55 U.S.-based organizations that 
published policy papers in the past four years. Those 
organizations include: think tanks and policy institutes 
(19); university institutes and research programs (14); 
civil society organizations, associations, and consortiums 
(9); industry and consultancy organizations (9); and 
government agencies (4).3 A policy paper in this section 
is defined as a research paper, research report, brief, or 
blog post that addresses issues related to AI and makes 
specific recommendations to policymakers. Topics of 
those papers are divided into primary and secondary 
categories: A primary topic is the main focus of the paper, 
while a secondary topic is a subtopic of the paper or an 
issue that was briefly explored.
Figure 5.1.11 plots the total number of U.S.-based AI-
related policy papers that have been published from 
2018 to 2021, which can proxy the general interest in AI 
within the U.S. policymaking space. The total number of 
policy papers has tripled since 2018, peaking in 2020 with 
273, and decreasing slightly in 2021, with 210.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2018
2019
2020
2021
0
50
100
150
200
250
Number of Policy Papers
210
NUMBER of AI-RELATED POLICY PAPERS by U.S.-BASED ORGANIZATIONS, 2018–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.11
3 The complete list of organizations the Index followed can be found in the Appendix.


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By Topic
In 2021, the leading primary topics were Privacy, Safety, 
and Security; Innovation and Technology; and Ethics 
(Figure 5.1.12). Certain topics, such as government and 
public administration, education and skills, as well as 
democracy, did not feature prominently as primary 
topics, but they were reported on more frequently 
as secondary topics. Among the AI topics to receive 
comparatively little attention from tracked organizations 
are those that relate to energy and the environment, 
humanities, physical sciences, and social and behavioral 
sciences.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
Primary Topic
Secondary Topic
0
20
40
60
0
20
40
60
Privacy, Safety, and Security
Innovation and Technology
Ethics
Int'l A"airs and Int'l Security
Industry and Regulation
Equity and Inclusion
Workforce and Labor
Gov't and Public Administration
Justice and Law Enforcement
Education and Skills
Communications and Media
Health and Biological Sciences
Social and Behavioral Sciences
Democracy
Physical Sciences
Energy and Environment
Humanities
36
59
34
29
62
62
33
23
51
51
15
0
4
2
7
1
1
30
63
36
45
45
29
58
58
57
13
51
17
17
3
3
1
1
NUMBER of AI-RELATED POLICY PAPERS by U.S.-BASED ORGANIZATIONS by TOPIC, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Number of Policy Papers
Figure 5.1.12


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FEDERAL BUDGET FOR NONDEFENSE  
AI R&D 
In December 2021, the National Science and Technology Council 
published a report on the public-sector AI R&D budget across 
departments and agencies participating in the Networking and 
Information Technology Research and Development (NITRD) 
program and the National Artificial Intelligence Initiative. The report 
does not include information on classified AI R&D investment by the 
defense and intelligence agencies.
In fiscal year (FY) 2021, nondefense U.S. government agencies 
allocated a total of $1.53 billion to AI R&D spending, approximately 
2.7 times what was spent in FY 2018 (Figure 5.2.1). This figure 
is projected to rise 8.8% for FY 2022, with a total of $1.67 billion 
requested.4 The increasing amount spent on AI R&D by nondefense 
departments indicates the U.S. government’s continued strong 
interest in public sector funding for AI research and development 
spanning a wide range of federal agencies.
5.2 U.S. PUBLIC INVESTMENT IN AI 
4 See NITRD website for details on AI R&D investment FY 2018-22 with the breakdown of core AI vs AI crosscut. Note that AI crosscutting budget data is not available for FY 2018.
Artificial Intelligence
Index Report 2022
This section examines the public AI investment in the United States, based on data from the U.S. government and Bloomberg Government.
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE
FY18 (ENACTED)
FY19 (ENACTED)
FY20 (ENACTED)
FY21 (ENACTED)
FY22 (REQUESTED)
0.00
0.50
1.00
1.50
Budget (in billions of U.S. Dollars)
0.56
1.43
1.53
1.67
1.11
U.S. FEDERAL BUDGET for NONDEFENSE AI R&D, FY 2018–22
Source: U.S. NITRD Program, 2022 | Chart: 2022 AI Index Report
Figure 5.2.1
The increasing amount 
spent on AI R&D by 
nondefense departments 
indicates the U.S. 
government’s continued 
strong interest in 
public sector funding 
for AI research and 
development spanning 
a wide range of federal 
agencies.


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U.S. DEPARTMENT OF DEFENSE 
BUDGET REQUEST
Spending on AI by the U.S. Department of Defense (DOD) 
can be proxied by looking at the publicly available 
requests made by the DOD for research, development, 
test, and evaluation (RDT&E) relating to AI. In FY 2021, 
DOD allocated $9.26 billion across 500 AI R&D programs 
(Figure 5.2.2), a 6.68% increase from the 8.68billionspentin2020.ForFY2022,thedepartmenthasrequested10 billion so far, which is likely to grow once additional 
requests and congressional appropriations are taken into 
account.
Important data caveat: This chart is indicative of one 
of the challenges of quantifying public AI spending. 
Bloomberg Government’s analysis that searches AI-
relevant keywords in DOD budgets shows that the 
department is requesting $10.0 billion for AI-specific R&D 
in FY 2022. However, DOD’s own measurement produces 
a smaller number of $874 million. The discrepancy 
may result from the difference in defining AI-related 
budget items. For example, a research project that uses 
AI for cyber defense may count human, hardware, and 
operations-related expenditures within the AI-related 
budget request, though the AI software component will 
be much smaller.
Sum of FY20 Funding
Sum of FY21 Funding
Sum of FY22 Funding
0
2
4
6
8
10
Budget (in billions of U.S. Dollars)
10.00
8.68
9.26
0.84: DOD Reported Budget on AI R&D
0.93: DOD Reported Budget on AI R&D
0.87: DOD Reported Budget on AI R&D
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH, DEVELOPMENT, TEST and EVALUATION (RDT&E), FY 2020–22
Source: Bloomberg Government and U.S. Department of Defense, 2021 | Chart: 2022 AI Index Report
Figure 5.2.2
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
DOD Top Five Highest-Funded Programs
This section highlight offers a more qualitative look at some of the AI-related research projects the 
DOD prioritizes. Table 5.2.1 presents the five DOD-related AI programs that received the greatest 
funding in 2021. In the past year, the DOD was interested in deploying AI for a number of purposes, 
from geospatial monitoring to reducing the threat posed by weapons of mass destruction. 
Program Name
Department
Funds Received 
(in millions)
Purpose
1 Rapid Capability 
Development and Maturation
Army
257 
Fund the development, engineering, acquisition, 
and operation of various AI-related technological 
prototypes that could be used for military purposes. 
2 Counter Weapons of 
Mass Destruction Advanced 
Technology Development
Defense Threat 
Reduction 
Agency
254 
Develop technologies that could “deny, defeat and 
disrupt” weapons of mass destruction (WMD).
3 Algorithmic Warfare  
Cross-Functional Teams –
Software Pilot Program 
Office of the 
Secretary of 
Defense
230 
Accelerate the integration of AI technologies in DOD 
systems to “improve warfighting speed and lethality.”
4 Joint Artificial Intelligence 
Center
Defense 
Information 
Systems 
Agency
137 
Develop, test, prototype, and demonstrate various AI 
and machine learning capabilities with the intention 
of integrating these capabilities across numerous 
domains which include “supply chain, personal 
recovery, infrastructure assessment, geospatial 
monitoring during disaster and cyber sense making.”
5 High Performance 
Computing Modernization 
Program
Army
96 
Investigate, demonstrate, and mature both general 
and special-purpose supercomputing environments 
that are used to satisfy wide-ranging DOD priorities.
Table 5.2.1
5.2 U.S. Public Investment in AI


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DOD AI R&D Spending by Department
DOD spending on AI R&D can also be broken down on 
a subdepartmental level, which reveals how individual 
defense agencies—the Army and the Navy, for instance—
compare in their AI spending (Figure 5.2.3). The U.S. 
Navy was the top-spending DOD agency in FY 2021 and 
is poised to maintain that position in 2022. They have 
requested a total of 1.86billioninFY2022forAI−relatedprojects,followedbytheArmy(1.77 billion), the Office 
of the Secretary of Defense (1.1billion)andtheAirForce(883 million).
FY20 (ENACTED)
FY21 (ENACTED)
FY22 (REQUESTED)
0
2
4
6
8
10
Budget (in billions of U.S. Dollars)
1.00
1.64
1.86
1.93
1.63
1.54
1.92
1.52
1.75
1.57
1.72
1.77
1.19
1.16
1.18
1.13
1.12
1.71
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH, DEVELOPMENT, TEST and EVALUATION (RDT&E) by
DEPARTMENT, FY 2020–22
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Air Force
Army
DARPA
DISA
Navy
OSD
Other
Figure 5.2.3
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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U.S. GOVERNMENT AI-RELATED 
CONTRACT SPENDING
Public investment in AI can also be measured by federal 
government spending on AI-related contracts. U.S. 
government agencies often award contracts to private 
companies for the supply of various goods and services 
that typically occupy the largest share of an agency’s 
budget. Bloomberg Government built a model to classify 
whether a U.S. government contract was AI-related by 
adding up all contracting transactions that contain a set 
of more than 100 AI-specific keywords in their titles or 
descriptions.5
Total Contract Spending
In 2021, federal departments and agencies spent a total of 
1.79billiononAI−relatedcontracts.AlthoughthisamountisnearlydoublewhatwasspentonAI−relatedcontractsin2018(roughly920 million), it represents a slight decrease 
from the amount spent on AI-related contracts in 2020, 
which peaked at $1.97 billion (Figure 5.2.4).
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0.0
0.5
1.0
1.5
2.0
Contract Spending (in billions of U.S. Dollars)
1.79
U.S. GOVERNMENT TOTAL CONTRACT SPENDING on AI, FY 2000–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.4
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE
5 Note that contractors may add a number of keywords into their applications during the procurement process, so some of the projects included may have a relatively small AI component relative to 
other parts of technology.


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Contract Spending by Department and Agency
Figures 5.2.5 and 5.2.6 report AI-related contract spending 
by the top 10 federal agencies in 2021 and from 2000 to 
2021, respectively. The DOD outspent the rest of the U.S. 
government on both charts by a significant margin. In 
2021, it spent $1.14 billion on AI-related contracts, roughly 
five times what was spent by the next highest department, 
the Department of Health and Human Services (234million).AggregatespendingonAIcontractsinthelastfouryearstellsasimilarstory.Since2018,theDODhasspent5.20 
billion on AI contracts, approximately seven times the next 
highest spender, NASA (1.41billion).Infact,since2018,theDODhasspenttwiceasmuchonAI−relatedcontractsasallothergovernmentagenciescombined.FollowingtheDODandNASAaretheDepartmentofHealthandHumanServices(700 million), the Department of Homeland 
Security (362million),andDepartmentoftheTreasury(156 million). 
0
200
400
600
800
1000
1200
Contract Spending (in millions of U.S. Dollars)
Department of Defense (DOD)
Department of Health and Human Services (HHS)
National Aeronautics and Space Administration (NASA)
Department of Homeland Security (DHS)
Department of Commerce (DOC)
Department of the Treasury (TREAS)
Department of Veterans A"airs (VA)
Department of Transportation (DOT)
Securities and Exchange Commission (SEC)
Department of Agriculture (USDA)
Department of Energy (DOE)
Agency for International Development (USAID)
Department of Justice (DOJ)
Department of State (DOS)
National Science Foundation (NSF)
1,138
234
159
49
38
25
81
12
12
12
6
4
8
3
2
TOP CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2021
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.5
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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0
1
2
3
4
5
Contract Spending (in billions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and Space Administration (NASA)
Department of Health and Human Services (HHS)
Department of Homeland Security (DHS)
Department of the Treasury (TREAS)
Department of Veterans A!airs (VA)
Department of Commerce (DOC)
Department of Agriculture (USDA)
Department of Energy (DOE)
Securities and Exchange Commission (SEC)
General Services Administration (GSA)
Department of State (DOS)
Social Security Administration (SSA)
Department of Transportation (DOT)
0.06
0.06
0.06
0.06
0.05
0.05
0.45
0.07
0.70
0.32
5.20
0.15
0.15
1.41
TOP CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2000–21 (SUM)
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.6
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
Largest Contract for Five Top-Spending  
Departments in 2021
To paint a better picture of how different U.S. government departments use AI, Table 5.2.2 shows the 
most expensive AI-related contract that the five highest AI-related-spending departments signed in 
2021. Last year, the U.S. government invested in AI to build autonomous vehicle prototypes, develop an 
AI imaging system that could assist with burn classification, and create robots capable of higher-level 
lunar navigation.
Contract Name
Department
Amount 
(in millions)
Purpose
Prototype Services in the Objective 
Areas of Automotive Cybersecurity, 
Vehicle Safety Technologies, Vehicle 
Light Weighting, Autonomous Vehicles 
and Intelligent Systems, Connected 
Vehicles, and Advanced Energy Storage 
Technologies
DOD
70 
To acquire prototypes in the domain of 
automotive cybersecurity, vehicle safety 
technologies, and autonomous vehicles and 
intelligent systems.
Biomedical Advanced Research and 
Development Authority (BARDA)
HHS
20 
To develop optical imaging devices and 
machine learning algorithms to assist 
in classifying and healing wounds and 
conventional burns. 
Commercial Lunar Payload Services
NASA
14
To develop lunar robots capable of navigating 
the moon’s south pole to acquire lunar 
resources and engage in lunar-based scientific 
activities. 
SBIR-Autonomous Surveillance  
Towers-Delivery Order
DHS
37
To construct towers capable of autonomous 
surveillance. 
Schedule 70: Information Technology 
DOC
13
To develop a prototype using AI technology 
that can improve patent search. 
Table 5.2.2
5.2 U.S. Public Investment in AI


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Chapter 5: AI Policy and Governance
APPENDIX
BLOOMBERG GOVERNMENT
Prepared by Amanda Allen
Bloomberg Government is a premium, subscription-
based service that provides comprehensive information 
and analytics for professionals who interact with—or 
are affected by—the government. Delivering news, 
analytics, and data-driven decision tools, Bloomberg 
Government’s digital workspace gives an intelligent edge 
to government affairs and contracting professionals. For 
more information or a demo, visit about.bgov.com.
 
Methodology
Contract Spending: Bloomberg Government’s Contracts 
Intelligence Tool structures all contracts data from 
www.fpds.gov. The CIT includes a model of government 
spending on artificial intelligence-related contracts that is 
based on a combination of government-defined product 
service codes and more than 100 AI-related keywords. 
For the section “U.S. Government Contract Spending,” 
Bloomberg Government analysts used contract spending 
data from fiscal year 2000 through fiscal year 2021.
Defense RDT&E Budget: Bloomberg Government 
organized all the RDT&E budget request line items 
available from the Defense Department Comptroller. For 
the section “U.S. Department of Defense (DOD) Budget,” 
Bloomberg Government used a set of AI-specific keywords 
to identify 500 unique budget activities related to artificial 
intelligence and machine learning worth a combined $5.9 
billion in FY 2021.
Legislative Documents: Bloomberg Government 
maintains a repository of congressional documents, 
including bills, Congressional Budget Office assessments, 
and reports published by congressional committees, 
the Congressional Research Service, and other offices. 
Bloomberg Government also ingests state legislative 
bills. For the section “AI Policy and Governance,” 
Bloomberg Government analysts identified all legislation, 
congressional committee reports, and CRS reports that 
referenced one or more AI-specific keywords.
APPENDIX


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GLOBAL LEGISLATION RECORDS ON AI
For AI-related bills passed into laws, the AI Index performed searches of the keyword “artificial intelligence,” in respective 
languages, on the websites of 25 countries’ congresses or parliaments, in full-text of bills. Note that only laws passed 
by state-level legislative bodies and signed into law (i.e., by presidents or received royal assent) from 2015 to 2021 are 
included. Future AI Index reports hope to include analysis on other types of legal documents, such as regulations and 
standards, adopted by state- or supranational-level legislative bodies, government agencies, etc.
Denmark
Website: https://www.retsinformation.dk/ 
Keyword: kunstig intelligen
Filter:
	
• Document Type: Laws
Finland
Website: https://www.finlex.fi/
Keyword: tekoäly 
Noting under the Current Legislation section
France 
Website: https://www.legifrance.gouv.fr/ 
Keyword: intelligence artificielle
Filter: 
	
• texte consolidé
	
• Document Type: Law
Germany
Website: http://www.gesetze-im-internet.de/index.html 
Keyword: künstliche Intelligenz
Filter: 
	
• 
All federal codes, statutes, and ordinances that are 
currently in force
	
• 
Volltextsuche (full text)
	
• 
Und-Verknüpfung der Wörter (entire word)
India 
Website: https://www.indiacode.nic.in 
Keyword: artificial intelligence
Note: The website used allows for a search of keywords 
in legalization title but not in the full text, as such it is not 
useful for this particular research. Therefore, a Google 
search using the “site” function to search the site with the 
keyword of “artificial intelligence” is conducted.
Australia
Website: www.legislation.gov.au 
Keyword: artificial Intelligence
Filters: 
	
• Legislation types: Acts
	
• 
Portfolios: Department of House of Representatives, 
Department of Senate
Note: Texts in explanatory memorandum are not counted.
Belgium
Website: http://www.ejustice.just.fgov.be/loi/loi.htm  
Keyword: intelligence artificielle
Brazil
Website: https://www.camara.leg.br/legislacao  
Keyword: inteligência artificial
Filter:
	
• Federal legislation
	
• Type: Law
Canada
Website: https://www.parl.ca/legisinfo/ 
Keyword: artificial Intelligence
Note: Results were investigated to determine how many of 
the bills introduced were eventually passed (i.e., received 
royal assent) and bill status was recorded.
China
Website: https://flk.npc.gov.cn/  
Keyword: 人工智能
Filters: 
	
• 
Legislative body: Standing Committee of the 
National People’s Congress
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Ireland
Website: www.irishstatutebook.ie
Keyword: artificial intelligence
Italy
Website: https://www.normattiva.it/
Keyword: intelligenza artificiale
Filter: 
	
• 
Document Type: law
Japan
Website: https://elaws.e-gov.go.jp/
Keyword: 人工知能
Filter:
	
• 
Full text
	
• 
Law
Netherlands
Website: https://www.overheid.nl/ 
Keyword: kunstmatige intelligentie
Filter:
	
• 
Document Type: Wetten
New Zealand
Website: www.legislation.govt.nz
Keyword: Artificial intelligence
Filter: 
	
• 
Document type: acts
	
• 
Status option: For the status option (example: acts in 
force, current bills, etc.) 
Norway
Website: https://lovdata.no/
Keyword: kunstig intelligens
Russia
Website: http://graph.garant.ru:8080/SESSION/PILOT/
main.htm (Database “The Federal Laws” in the official 
website of the Federation Council of the Federal Assembly 
of the Russian Federation.)
Keyword: искусственный интеллект
Filter:
	
• 
Words in text
Singapore
Website: https://sso.agc.gov.sg/
Keyword: artificial intelligence
Filter: 
	
• 
Document Type: Current acts and subsidiary 
legislation
South Africa
Website: www.gov.za
Keyword: artificial intelligence
Filter: 
	
• 
Document: acts
Note: This search function seemingly does not search 
within the context of the full text and so no results were 
returned. Therefore, a Google search using the “site” 
function to search the site with the keyword of “artificial 
intelligence” is conducted. 
South Korea
Website: https://law.go.kr/eng/; https://elaw.klri.re.kr/
Keyword: artificial Intelligence or 인공 지능
Filter:
	
• 
Type: Act 
Note: Cannot search combined words, so individual 
analysis is conducted. 
Spain 
Website: https://www.boe.es/ 
Keyword: inteligencia artificial
Filter:
	
• 
Type: law
	
• 
Head of state (for passed laws)
Sweden
Website: https://www.riksdagen.se/
Keyword: artificiell intelligens
Filter: Swedish Code of Statutes
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Switzerland
Website: https://www.fedlex.admin.ch/ 
Keyword: intelligence artificielle
Filter: 
	
• 
Text category: federal constitution, federal acts, and 
federal decrees, miscellaneous texts, orders, and 
other forms of legislation.
	
• 
Publication period for legislation was limited to 
2015-2021.  
United Kingdom
Website: https://www.legislation.gov.uk/
Keyword: artificial intelligence
Filter:
	
• 
Legislation Type: U.K. Public General Acts & U.K. 
Statutory Instruments
United States
Website: https://www.congress.gov/ 
Keyword: artificial intelligence
Filter:
	
• 
Source: Legislation
Status of legislation: Became law
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MENTIONS OF AI IN AI-RELATED LEGISLATION PROCEEDINGS
For mentions of AI in AI-related legislative proceedings around the world, the AI Index performed searches of the keyword 
“artificial intelligence,” in respective languages, on the websites of 25 countries’ congresses or parliaments, usually under 
sections named “minutes,” “hansard,” etc.
Denmark
Website: https://www.retsinformation.dk/ 
Keyword: kunstig intelligens
Filter:
	
• Minutes
Finland
Website: https://www.eduskunta.fi/ 
Keyword: tiedot
Filter:
	
• Parliamentary Affairs and Documents
	
• Public document: Minutes
	
• Actor: Plenary sessions
France 
Website: https://www.assemblee-nationale.fr/ 
Keyword: intelligence artificielle
Filter: 
	
• Reports of the debates in session
Note: Such documents were only prepared starting in 
2017.
Germany
Website: https://dip.bundestag.de/ 
Keyword: künstliche Intelligenz
Filter: 
	
• Speeches, requests to speak in the plenum
India 
Website: http://loksabhaph.nic.in/ 
Keyword: artificial intelligence
Filter:
	
• Exact word/phrase
Ireland
Website: https://www.oireachtas.ie/ 
Keyword: artificial intelligence
Filter: Content of parliamentary debates
Australia
Website: https://www.aph.gov.au/Parliamentary_Business/
Hansard 
Keyword: artificial intelligence
Belgium
Website: http://www.parlement.brussels/search_form_fr/ 
Keyword: intelligence artificielle
Filter
	
• Document Type: all
Brazil
Website: https://www2.camara.leg.br/atividade-legislativa/
discursos-e-notas-taquigraficas  
Keyword: inteligência artificial
Filter:
	
• Federal legislation
	
• Type: Law
Canada
Website: https://www.ourcommons.ca/PublicationSearch/
en/?PubType=37 
Keyword: artificial Intelligence
China
Website: Various reports on the work of the government
Keyword: 人工智能
Note: The National People’s Congress is held once per 
year and does not provide full legislative proceedings. 
Hence, the counts included in the analysis only searched 
the mentions of artificial intelligence in the only public 
document released from the Congress meetings, the 
Report on the Work of the Government, delivered by the 
Premier. 
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Italy
Website: https://aic.camera.it/aic/search.html 
Keyword: intelligenza artificiale
Filter: 
	
• Type: All
	
• Search by exact phrase
Japan
Website: https://kokkai.ndl.go.jp/#/  
Keyword: 人工知能
Filter:
	
• Full text
	
• Law
Netherlands
Website: https://www.tweedekamer.nl/kamerstukken?pk_
campaign=breadcrumb  
Keyword: kunstmatige intelligentie
Filter:
	
• Parliamentary papers - Plenary reports
New Zealand
Website: https://www.parliament.nz/en/pb/hansard-
debates/  
Keyword: artificial intelligence 
Norway
Website: https://www.stortinget.no/no/Saker-og-
publikasjoner/Publikasjoner/Referater/ 
Keyword: kunstig intelligens
Note: This search function does not directly allow the 
keyword within minutes. Therefore, a Google search using 
the “site” function to search the site with the keyword of 
“artificial intelligence” is conducted. 
Russia
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Chapter 5: AI Policy and Governance
APPENDIX


31
Artificial Intelligence
Index Report 2022
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Congressional record section: Senate, House of 
Representatives, and Extensions of Remarks
U.S. AI POLICY PAPERS
Organizations
To develop a more nuanced understanding of the thought 
leadership that motivates AI policy, we tracked policy 
papers published by 55 organizations in the United States 
or with a strong presence in the United States (expanded 
from the list of 36 organizations last year) across four 
broad categories: 
	
• 
Civil Society, Associations & Consortiums: 
Algorithmic Justice League, Alliance for Artificial 
Intelligence in Healthcare, Amnesty International, 
EFF, Future of Privacy Forum, Human Rights Watch, 
IJIS Institute, Institute for Electrical and Electronics 
Engineers, Partnership on AI
	
• 
Consultancy: Accenture, Bain & Company, Boston 
Consulting Group, Deloitte, McKinsey & Company
	
• 
Government Agencies: Congressional Research 
Service, Library of Congress, Defense Technical 
Information Center, Government Accountability 
Office, Pentagon Library
	
• 
Private Sector Companies: Google AI, Microsoft AI, 
Nvidia, OpenAI
	
• 
Think Tanks & Policy Institutes: American Enterprise 
Institute, Aspen Institute, Atlantic Council, Brookings 
Institute, Carnegie Endowment for International 
Peace, Cato Institute, Center for a New American 
Security, Center for Strategic and International 
Studies, Council on Foreign Relations, Heritage 
Foundation, Hudson Institute, MacroPolo, National 
Security Institute, New America Foundation, RAND 
Corporation, Rockefeller Foundation, Stimson 
Center, Urban Institute, Wilson Center
	
• 
University Institutes & Research Programs: AI and 
Humanity Cornell University; AI Now Institute, 
New York University; AI Pulse, UCLA Law; Belfer 
Center for Science and International Affairs, 
Harvard University; Berkman Klein Center, Harvard 
University; Center for Information Technology 
Policy, Princeton University; Center for Long-Term 
Cybersecurity, UC Berkeley; Center for Security 
and Emerging Technology, Georgetown University; 
CITRUS Policy Lab, UC Berkeley; Hoover Institution; 
Institute for Human-Centered Artificial Intelligence, 
Stanford University; Internet Policy Research 
Initiative, Massachusetts Institute of Technology; 
MIT Lincoln Laboratory; Princeton School of Public 
and International Affairs
Methodology
Each broad topic area is based on a collection of 
underlying keywords that describe the content of the 
specific paper. We included 17 topics that represented the 
majority of discourse related to AI between 2018-2021. 
These topic areas and the associated keywords are listed 
below:
	
• 
Health & Biological Sciences: medicine, healthcare 
systems, drug discovery, care, biomedical research, 
insurance, health behaviors, COVID-19, global health
	
• 
Physical Sciences: chemistry, physics, astronomy, 
earth science
	
• 
Energy & Environment: energy costs, climate 
change, energy markets, pollution, conservation, oil 
and gas, alternative energy
	
• 
International Affairs & International Security: 
international relations, international trade, 
developing countries, humanitarian assistance, 
warfare, regional security, national security, 
autonomous weapons
	
• 
Justice & Law Enforcement: civil justice, criminal 
justice, social justice, police, public safety, courts
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Artificial Intelligence
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Chapter 5: AI Policy and Governance
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• 
Communications & Media: social media, 
disinformation, media markets, deepfakes
	
• 
Government & Public Administration: federal 
government, state government, local government, 
public sector efficiency, public sector effectiveness, 
government services, government benefits, 
government programs, public works, public 
transportation
	
• 
Democracy: elections, rights, freedoms, liberties, 
personal freedoms
	
• 
Industry & Regulation: economy, antitrust, M&A, 
competition, finance, management, supply chain, 
telecom, economic regulation, technical standards, 
autonomous vehicle industry and regulation
	
• 
Innovation & Technology: advancements and 
improvements in AI technology, R&D, intellectual 
property, patents, entrepreneurship, innovation 
ecosystems, startups, computer science, engineering
	
• 
Education & Skills: early childhood, K-12, higher 
education, STEM, schools, classrooms, reskilling
	
• 
Workforce & Labor: labor supply and demand, talent, 
immigration, migration, personnel economics, future 
of work
	
• 
Social & Behavioral Sciences: sociology, linguistics, 
anthropology, ethnic studies, demography, 
geography, psychology, cognitive science
	
• 
Humanities: arts, music, literature, language, 
performance, theater, classics, history, philosophy, 
religion, cultural studies
	
• 
Equity & Inclusion: biases, discrimination, gender, 
race, socioeconomic inequality, disabilities, 
vulnerable populations
	
• 
Privacy, Safety & Security: anonymity, GDPR, 
consumer protection, physical safety, human control, 
cybersecurity, encryption, hacking
	
• 
Ethics: transparency, accountability, human 
values, human rights, sustainability, explainability, 
interpretability, decision-making norms


1 
 
 
 
 
 
 
 
On July 20, China's State Council issued a seminal document, entitled A Next 
Generation Artificial Intelligence Development Plan. This important aspirational document 
sets out a top-level design blueprint charting the country's approach to developing artificial 
intelligence (AI) technology and applications, setting broad goals up to 2030. 
  
Please find the full text of the document below. 
 
The translators produced analysis on the new document and Chinese AI ambitions for New 
America here. 
  
The document has been translated into English by a group of experienced Chinese 
linguists with deep backgrounds on the subject matter and on China's S&T establishment 
and current AI capabilities.  They are: Rogier Creemers, Leiden Asia Centre; Graham 
Webster, Yale Law School Paul Tsai China Center; Paul Triolo, Eurasia Group; and Elsa Kania. 
 
The group is grateful to New America Cybersecurity Initiative Fellow John Costello for 
comments that helped to improve the translation. 
   
Any errors in translation are the responsibility of the translators, and we welcome 
comments, which can be directed to the collaborators at this 
address: chinacomments@newamerica.org 
 
 
 


2 
State Council Notice on the Issuance of the Next 
Generation Artificial Intelligence Development Plan 
Completed: July 8, 2017 
Released: July 20, 2017 
 
A Next Generation Artificial  
Intelligence Development Plan 
 
The rapid development of artificial intelligence (AI) will profoundly change human society 
and life and change the world. To seize the major strategic opportunity for the development 
of AI, to build China’s first-mover advantage in the development of AI, to accelerate the 
construction of an innovative nation and global power in science and technology, in 
accordance with the requirements of the CCP Central Committee and the State Council, this 
plan has been formulated. 
I.  The Strategic Situation 
 
The development of AI has entered a new stage. After sixty years of evolution, especially in 
mobile Internet, big data, supercomputing, sensor networks, brain science, and other new 
theories and new technologies, under the joint impetus of powerful demands of economic 
and social development, AI’s development has accelerated, displaying deep learning, 
cross-domain integration, man-machine collaboration, the opening of swarm intelligence, 
autonomous control, and other new characteristics. Big data-driven cognitive learning, 
cross-media collaborative processing, and man-machine collaboration–strengthened 
intelligence, swarm integrated intelligence, and autonomous intelligent systems have 
become the focus of the development of AI. The results of brain science research inspired 
human-like intelligence that awaits action; the trends involving the chips, hardware, and 
platform have become apparent; the development of AI has entered into a new stage. At 
present, the development a new generation of AI and related disciplines, theoretical 
modeling, technological innovation, hardware and software upgrades, etc., all advance, 
provoking chain-style breakthroughs, promoting the acceleration of the elevation of 
economic and social domains from digitization and networkization to intelligentization.  
 
AI has become a new focus of international competition. AI is a strategic technology that 
will lead in the future; the world’s major developed countries are taking the development of 
AI as a major strategy to enhance national competitiveness and protect national security; 
intensifying the introduction of plans and strategies for this core technology, top talent, 
standards and regulations, etc.; and trying to seize the initiative in the new round of 
international science and technology competition. At present, China’s situation in national 
security and international competition is more complex, and [China] must, looking at the 
world, take the development of AI to the national strategic level with systemic layout, take 
the initiative in planning, firmly seize the strategic initiative in the new stage of 
international competition in AI development, to create new competitive advantage, 
opening up the development of new space, and effectively protecting national security. 
 
AI has become a new engine of economic development. AI has become the core driving 
force for a new round of industrial transformation, [which] will advance the release of the 


3 
huge energy stored from the previous scientific and technological revolution and industrial 
transformation, and create a new powerful engine, reconstructing production, distribution, 
exchange, consumption, etc., links in economic activities; with new demands taking shape 
from the macro to the micro within each domain of intelligentization; with the birth of new 
technologies, new products, new industries, new formats, new models; triggering 
significant changes in economic structure, profound changes in human modes of 
production, lifestyle, and thinking; and a whole leap of achieving social productivity. 
China’s economic development enters a new normal, deepening the supply side of 
structural reform task is very arduous, [and China] must accelerate the rapid application of 
AI, cultivating and expanding AI industries to inject new kinetic energy into China’s 
economic development. 
 
AI brings new opportunities for social construction. China is currently in the decisive stage 
of comprehensively constructing a moderately prosperous society. The challenges of 
population aging, environmental constraints, etc., remain serious. The widespread use of AI 
in education, medical care, pensions, environmental protection, urban operations, judicial 
services, and other fields will greatly improve the level of precision in public services, 
comprehensively enhancing the people’s quality of life. AI technologies can accurately 
sense, forecast, and provide early warning of major situations for infrastructure facilities 
and social security operations; grasp group cognition and psychological changes in a 
timely manner; and take the initiative in decision-making and reactions—which will 
significantly elevate the capability and level of social governance, playing an irreplaceable 
role in effectively maintaining social stability. 
 
The uncertainties in the development of AI create new challenges. AI is a disruptive 
technology with widespread influence that may cause: transformation of employment 
structures; impact on legal and social theories; violations of personal privacy; challenges in 
international relations and norms; and other problems. It will have far-reaching effects on 
the management of government, economic security, and social stability, as well as global 
governance. While vigorously developing AI, we must attach great importance to the 
potential safety risks and challenges, strengthen the forward-looking prevention and 
guidance on restraint, minimize risk, and ensure the safe, reliable, and controllable 
development of AI. 
 
China possesses a favorable foundation for the development of AI. The nation has: 
deployed the National Key Research and Development Plan’s key special projects, such as 
intelligent manufacturing; issued and implemented the “Internet +” and AI Three-Year 
Activities and Implementation Program, releasing a series of measures from science and 
technology research and development; and promoted applications and industrial 
development, and other aspects. As a result of many years of continuous accumulation, 
China has achieved important progress in the field of AI, with the number of international 
scientific and technology papers published and the number of inventions patented ranked 
second in the world, while achieving important breakthroughs in certain domains of core 
crucial technologies. Leading the world in voice recognition and visual recognition 
technologies; initially possessing the capability for leapfrog development in adaptive 
autonomous learning, intuitive sensing, comprehensive reasoning, hybrid intelligence, and 
swarm intelligence, etc.; with Chinese information processing, intelligent monitoring, 
biometric identification, industrial robots, service robots, and unmanned driving gradually 
entering practical application; AI innovation and entrepreneurship have become 
increasingly active, and a number of leading enterprises have accelerated their growth, 


4 
receiving widespread concern and recognition internationally. Accelerate the accumulation 
of technological capabilities and massive data resources, the organization integration of 
both the huge demand for applications and an open market environment, which together 
constitute China’s unique advantage in AI development. 
 
At the same time, we must also clearly see that there is still a gap between China’s overall 
level of development of AI relative to that of developed countries—lacking major original 
results in the basic theory, core algorithms, key equipment, high-end chips, major products 
and systems, foundational materials, components, software and interfaces, etc. Scientific 
research institutions and enterprises do not yet possess international influence upon 
ecological cycles and supply chain, lacking a systematic research and development layout; 
cutting-edge talent for AI is far from meeting demand. Adapting to the development of AI 
requires the urgent improvement of basic infrastructure, policies and regulations, and 
standards systems. 
 
Facing a new situation and new demands, we must take the initiative to pursue and adapt 
to change, firmly seize the major historic opportunity for the development of AI, stick 
closely to development, study and evaluate the general trends, take the initiative to plan, 
grasp the direction, seize the opportunity, lead the world in new trends in the development 
of AI, serve economic and social development, and support national security, promoting 
the overall elevation of the nation’s competitiveness and leapfrog development. 
 
II.  The Overall Requirements 
(1)  Guiding Ideology 
  
Comprehensively implement the spirit of the 18th Party Congress and 18th Central 
Committee’s Third, Fourth, Fifth, and Sixth Plenary Sessions. Thoroughly study and 
implement the spirit of General Secretary Xi Jinping’s series of important sayings and new 
concepts, new ideas, and new strategy for governing the country; according to the “five in 
one” overall layout and “four comprehensives” strategic layout, conscientiously implement 
the CPC Central Committee and State Council decision-making arrangements, deeply 
implement the innovation-driven development strategy to accelerate the deep integration 
of AI with the economy, society and national defense as a primary line, to enhance: 
scientific and technological innovation capacity for a new generation of AI as the main 
direction of attack; intelligent economy development; smart society construction; 
protecting national security; building of knowledge clusters, technology clusters, and 
industry clusters mutually integrated with talent, system, and culture, for a mutually 
supporting ecosystem, advancing intelligentization as the center of humanity’s sustainable 
development. Comprehensively enhance society’s productive forces, comprehensive 
national power, and national competitiveness, in order to provide strong support to 
accelerate the construction of an innovative new-type nation and global science and 
technology power, to achieve the two centennial goals and the great rejuvenation of the 
Chinese nation. 


5 
 
(2)  The Basic Principles 
  
Technology-Led. Grasp the global development trend of AI, highlight the deployment of 
forward-looking research and development, explore the layout in key frontier domains, 
long-term support, and strive to achieve transformational and disruptive breakthroughs in 
theory, methods, tools, and systems; comprehensively enhance original innovation 
capability in AI, accelerate the construction of a first-mover advantage, to achieve high-
end leading development. 
  
Systems Layout. According to the different characteristics of foundational research, 
technological research and development, industrial development, and commercial 
applications, formulate a targeted systems development strategy. Fully give play to the 
advantages of the socialist system to concentrate forces to do major undertakings, 
promote the planning and layout of projects, bases, and a talent pool, organically link 
already-deployed major projects and new missions, continue current urgent needs and 
long-term development echelons, construct innovation capacity, create a collaborative 
force for institutional reforms and the policy environment. 
 
Market-Dominant. Follow the rules of the market, remain oriented toward application, 
highlight companies’ choices on the technological line and primary role in the development 
of commercial product standards, accelerate the commercialization of AI technology and 
results, and create a competitive advantage. Grasp well the division of labor between 
government and the market, better take advantage of the government in planning and 
guidance, policy support, security and guarding, market regulation, environmental 
construction, the formulation of ethical regulations, etc.  
 
Open-Source and Open. Advocate the concept of open-source sharing, and promote the 
concept of industry, academia, research, and production units each innovating and in 
principal pursuing joint innovation and sharing. Follow the coordinated development law for 
economic and national defense construction; promote two-way conversion and application 
for military and civilian scientific and technological achievements and co-construction and 
sharing of military and civilian innovation resources; form an all-element, multi-domain, 
highly efficient new pattern of civil-military integration. Actively participate in global 
research and development and management of AI, and optimize the allocation of 
innovative resources on a global scale. 
(3)  Strategic Objectives 
  
These are divided into the following three steps: 
 
First, by 2020, the overall technology and application of AI will be in step with globally 
advanced levels, the AI industry will have become a new important economic growth point, 
and AI technology applications will have become a new way to improve people’s 
livelihoods, strongly supporting [China’s] entrance into the ranks of innovative nations and 
comprehensively achieving the struggle toward the goal of a moderately prosperous 
society. 


6 
  
● By 2020 China will have achieved important progress in a new generation of AI 
theories and technologies. It will have actualized important progress in big data 
intelligence, cross-medium intelligence, swarm intelligence, hybrid enhanced 
intelligence, and autonomous intelligence systems, and will have achieved 
important progress in other foundational theories and core technologies; the 
country will have achieved iconic advances in AI models and methods, core devices, 
high-end equipment, and foundational software. 
● The AI industry’s competitiveness will have entered the first echelon internationally. 
China will have established initial AI technology standards, service systems, and 
industrial ecological system chains. It will have cultivated a number of the world's 
leading AI backbone enterprises, with the scale of AI’s core industry exceeding 150 
billion RMB, and exceeding 1 trillion RMB as driven by the scale of related industries. 
● The AI development environment will be further optimized, opening up new 
applications in important domains, gathering a number of high-level personnel and 
innovation teams, and initially establishing AI ethical norms, policies, and 
regulations in some areas. 
 
Second, by 2025, China will achieve major breakthroughs in basic theories for AI, such that 
some technologies and applications achieve a world-leading level and AI becomes the 
main driving force for China’s industrial upgrading and economic transformation, while 
intelligent social construction has made positive progress. 
  
● By 2025, a new generation of AI theory and technology system will be initially 
established, as AI with autonomous learning ability achieves breakthroughs in many 
areas to obtain leading research results.  
● The AI industry will enter into the global high-end value chain. This new-generation 
AI will be widely used in intelligent manufacturing, intelligent medicine, intelligent 
city, intelligent agriculture, national defense construction, and other fields, while 
the scale of AI’s core industry will be more than 400 billion RMB, and the scale of 
related industries will exceed 5 trillion RMB.  
● By 2025 China will have seen the initial establishment of AI laws and regulations, 
ethical norms and policy systems, and the formation of AI security assessment and 
control capabilities. 
 
Third, by 2030, China’s AI theories, technologies, and applications should achieve world-
leading levels, making China the world’s primary AI innovation center, achieving visible 
results in intelligent economy and intelligent society applications, and laying an important 
foundation for becoming a leading innovation-style nation and an economic power.   
 
● China will have formed a more mature new-generation AI theory and technology 
system. The country will achieve major breakthroughs in brain-inspired intelligence, 
autonomous intelligence, hybrid intelligence, swarm intelligence, and other areas, 
having important impact in the domain of international AI research and occupying 
the commanding heights of AI technology. 
● AI industry competitiveness will reach the world-leading level. AI should be 
expansively deepened and greatly expanded into production and livelihood, social 
governance, national defense construction, and in all aspects of applications, will 
become an expansive core technology for key systems, support platforms, and the 
intelligent application of a complete industrial chain and high-end industrial 


7 
clusters, with AI core industry scale exceeding 1 trillion RMB, and with the scale of 
related industries exceeding 10 trillion RMB. 
● China will have established a number of world-leading AI technology innovation and 
personnel training centers (or bases), and will have constructed more 
comprehensive AI laws and regulations, and an ethical norms and policy system. 
(4)  Overall Deployment 
  
The development of AI is a complex systemic project related to the overall situation, that 
must be arranged in accordance with “build one system, grasp the two attributes, adhere 
to the trinity, and strengthen the four supports” to form a strategic path for the healthy and 
sustainable development of AI. 
 
Construct an open and cooperative AI technology innovation system. Target the weak 
foundation in original theories, and the key difficulties and deficiencies in major products 
and systems. Establish foundational theories and a common technology system for a new 
generation of AI, laying out the construction of a major scientific and technological 
innovation base. Strengthen the high-end talent team in AI to promote innovation and 
cooperative interactions. Form a continuous innovation capability for AI. 
 
Grasp AI’s characteristic high degree of integration of technological attributes and social 
attributes. It is necessary not only to increase efforts in the research and development and 
applications of AI, maximizing the potential of AI, but also to predict AI’s challenges, 
coordinate industrial policies, innovate in policies and social policies, achieve the 
coordination of encouraging development and reasonable regulation, and maximize risk 
prevention.  
 
Adhere to the promotion of the trinity of breakthroughs in AI research and development, 
product applications, and fostering industry development. Adapt to the characteristics and 
trends of AI development. Strengthen the deep integration of the innovation chain and 
industrial chain, the interactive evolution of technology supply and market demand. Take 
technological breakthroughs to promote domain applications and industrial upgrading. 
Through application demonstrations, promote the optimization of technologies and 
systems. At the same time as greatly promoting technology applications and industrial 
development, strengthen long-term R&D layout and research. Achieve rolling development 
and continuous improvement. Ensure that theory is in the front, the technological 
commanding heights are occupied, and applications are secure and controllable. 
  
Fully support science and technology, the economy, social development, and national 
security.  Drive comprehensive elevation on national innovative capability with AI 
technological breakthroughs. Lead in the process of constructing a global science and 
technology power. Through strengthening intelligent industry and cultivating the intelligent 
economy, create a new growth cycle for China’s next decade or even decades of economic 
prosperity. Through building an intelligent society, promote the improvement of people’s 
livelihoods and welfare and implement people-centric development thinking. Through AI, 
elevate national defense strength and assure and protect national security.  


8 
III.  Focus Tasks 
 
Based on the overall picture of national development, accurately grasp the global 
development trends of AI, find the correct openings for breakthroughs and directions for 
the main thrust, comprehensively strengthen basic science and technology innovation 
capabilities, comprehensively expand the depth and breadth of application in focus areas, 
and comprehensively enhance the built-in intelligence levels of applications in economic 
and social development, as well as in national defence. 
(1)  Build open and coordinated AI science and technology 
innovation systems 
 
Focus on increasing the supply of AI innovation sources; strengthen deployments in areas 
such as advanced basic theory, key general technologies, basic platforms, talent teams, 
etc.; stimulate open-source sharing; systematically enhance sustained innovation 
capabilities; ensure that our country's AI science and technology levels ascend to the 
leading global ranks; and make ever more contributions to the development of global AI. 
1. Establish basic theory systems for a new generation of AI 
 
Focus on major advanced scientific AI questions; concurrently deal with present needs and 
long-term developments; make breakthroughs in basic AI application theory bottlenecks; 
give priority to deploying basic research that may trigger paradigmatic change in AI; 
stimulate the intersection and convergence of disciplines; and provide powerful scientific 
reserves for the sustained development and profound application of AI. 
 
Make breakthroughs in basic application theory bottlenecks. Aim at basic theoretical 
orientations with clear applied objectives, which promise to trigger an upgrade of AI 
technology, strengthen basic theoretical research on big data intelligence, cross-media 
sensing and computing, human-machine blended intelligence, mass intelligence, 
autonomous cooperation and decision-making, etc. Focus on breakthroughs in big data 
intelligence, unsupervised learning, comprehensive deep reasoning and other such difficult 
issues. Establish data-driven cognitive computing models with natural language 
understanding at the core, and shape capabilities to go from big data to knowledge, and 
from knowledge to decision-making. Focus on breakthroughs in cross-media sensing and 
computing theory, including theories and methods for: low-cost and low-energy smart 
sensing, active sensing in complex landscapes, listening comprehension in the natural 
environment as well as language sensing, autonomous multimedia learning, etc. Realize 
superhuman sensing and highly-dynamic, high-dimensional, and multi-model distributed 
large-landscape sensing. The focuses on breakthroughs in blended and enhanced 
intelligence theory are: theories on human-machine cooperative and blended 
environmental understanding, decision-making, and learning; intuitive reasoning and 
causal models, recall and knowledge evolution, etc.; realizing blended and enhanced 
intelligence where learning and reflection approach or exceed human intelligence levels. 
The focuses for breakthroughs in collective intelligence theory are: theories and methods 
for the organization, emergence and learning of collective intelligence; establishment of 
expressible and computable mass intelligence incentive algorithms and models; and 
shaping Internet-based collective intelligence theory systems. The focuses for 


9 
breakthroughs in autonomous coordination, control and optimized decision-making theory 
are: theories concerning coordination sensing and interaction aimed at autonomous 
unmanned systems; autonomous coordination control and optimized decision-making; 
knowledge-driven human-machine-object triangular coordination and interoperation, etc.; 
and shaping novel theoretical systems and frameworks for innovation in autonomous 
intelligence and unmanned systems. 
 
Arrange advanced basic theoretical research. Aim for a direction that may trigger a 
paradigmatic change in AI, far-sightedly arrange research on high-level machine learning, 
brain-inspired intelligence computing, quantum smart computing, and other such cross-
domain basic theories. The focuses for breakthroughs in high-level machine learning 
theory are theories and methods concerning self-adaptive learning, autonomous learning, 
etc., and realizing AI with high interpretative and strong generalization capabilities. The 
focuses for breakthroughs in brain-inspired intelligence computing theory are: theories 
concerning brain-inspired information encoding, processing, recall, learning and reasoning; 
the creation of brain-inspired complex systems and brain-inspired control theories and 
methods; and establishment of new large-scale brain-inspired intelligence computing 
models and brain-inspired understanding computing models. The focuses for 
breakthroughs in quantum computing theory are: methods for quantum-accelerated 
machine learning; establishment of high-performance computing and quantum computing 
convergence models; and shaping high-efficiency, accurate, and autonomous quantum AI 
system setups. 
 
Launch cross-disciplinary exploratory research. Promote the intersection and convergence 
of AI with neurology, cognitive science, quantum science, psychology, mathematics, 
economics, sociology and other such related basic disciplines; strengthen basic theoretical 
mathematical research to guide the development of AI algorithms and models; focus on 
researching the basic theoretical questions of AI legal principles; support exploratory 
research that is strongly original, and where there is no consensus; encourage scientists to 
explore freely; dare to overcome front-line scientific difficulties in AI; create ever more 
original theory; and make ever more original discoveries. 
 
Box 1: Basic Theories 
1. Big data intelligence theory. Research new data-driven and knowledge-driven AI 
methods, theories and methods for sensing computing theory with natural language 
understanding, images and figures at the core, comprehensive deep reasoning and 
creative AI theories and methods, basic theories and frameworks on smart decision-
making with incomplete information, data-driven common AI data models and 
theories, etc. 
2. Cross-media sensing and computing theory. Research sensing that exceeds human 
visual abilities, active visual sensing and computing aimed at the real world, 
auditory sensing and computing of natural acoustic scenes, language sensing and 
computing in an environment of natural interaction, human sensing and computing 
aimed at asynchronous orders, autonomous learning aimed at smart media sensing, 
and urban omnidimensional smart sensing and reasoning engines. 
3. Hybrid and enhanced intelligence theory. Research hybridization and convergence 
where “the human is in the loop,” behavioral strengthening through human-
machine smart symbiosis and brain-machine coordination, intuitive machine 


10 
reasoning and causal models, associative recall models and knowledge evolution 
methods, complex data and task blended and enhanced intelligence learning 
methods, cloud robotics coordination computing methods, and situational 
comprehension and human-machine group coordination in real-world 
environments. 
4. Swarm intelligence theory. Research swarm intelligence structural theory and 
organizational methods, swarm intelligence incentive mechanisms and emergence 
mechanisms, swarm intelligence learning theories and methods, common swarm 
intelligence computing paradigms and models. 
5. Autonomous coordination and control, and optimized decision-making theory. 
Research coordination sensing and interaction aimed at autonomous unmanned 
systems, coordination, control and optimized decision-making aimed at 
autonomous and unmanned systems, knowledge-driven human-machine-object 
triangular coordination and interoperability theories. 
6. High-level machine learning theory. Research basic statistical learning theories, 
reasoning and decision-making under uncertainty, distributed learning and 
interaction, learning while protecting privacy, small-sample learning, deep intensive 
learning, unsupervised learning, semi-supervised learning, active learning and other 
such learning theories and efficient models. 
7. Brain-inspired intelligence computing theory. Research theories and methods on 
brain-inspired sensing, brain-inspired learning, and brain-inspired recall 
mechanisms and computing blends, brain-inspired complex systems, brain-inspired 
control, etc. 
8. Quantum intelligent computing theory. Explore cognitive quantum models and 
intrinsic mechanisms, research efficient quantum intelligence models and 
algorithms, high-performance and high-bitrate quantum AI processors, real-time 
quantum AI systems that can exchange information with the outside world, etc. 
 
2.  Build a next-generation AI key general technology system 
 
Focusing on the urgent need to raise China's international competitiveness in AI, next-
generation AI key general technology R&D and deployment should make algorithms the 
core; data and hardware the foundation; and upping capabilities in sensing and 
recognition, knowledge computing, cognitive reasoning, executing motion, and human-
machine interface the emphasis; in order to form openly compatible, stable and mature 
technological systems. 
 
Knowledge computing engine and knowledge service technology. Key breakthroughs in 
knowledge processing, deep search, and visual interactive core technology; realization of 
automatic acquisition of incrementally growing knowledge; possession of concept 
discernment, object discovery, attribute prediction, evolutionary knowledge modeling, and 
relationship discovery capabilities; the formation of multi-billion-scale, multi-source, 
multi-disciplinary, multi-data type, and cross-medium knowledge maps. 
 
Cross-medium analytical reasoning technology. Key breakthroughs in cross-medium 
unified indicators; relational understanding and knowledge mining; knowledge map 
structure and learning; knowledge evolution and reasoning; intelligent description and 


11 
generation, etc., technology. Realization of cross-medium knowledge indicators, analysis, 
mining, reasoning, evolution, and utilization. Construct analytic reasoning engines. 
 
Key swarm intelligence technology. Key breakthroughs on the basis of the popularization 
of the internet, mass collaboration, knowledge resource management, and open sharing, 
etc., technologies. Building frameworks to display swarm intelligence knowledge. Realize 
the integration and strengthening of swarm intelligence-based knowledge acquisition and 
swarm intelligence under open development conditions. Support swarm perception, 
cooperation, and evolution at a national, tens-of-millions scale.  
 
New architecture and new technology for hybrid and enhanced intelligence. Key 
breakthroughs in human-machine interaction for perception and execution integration 
models, new types of intelligent computing-fronted sensors, common use hybrid 
architecture, etc., core technologies. Build autonomous, environmentally adaptable hybrid 
enhanced intelligent systems, human-machine hybrid enhanced intelligent systems and 
support environments. 
 
Intelligent technologies of autonomous unmanned systems. Key breakthroughs in 
autonomous unmanned system computing architecture, complex situational environment 
perception and understanding, real-time accurate positioning, adaptable, intelligent 
navigation in complex environments, etc., general technologies. Unmanned and 
autonomously controlled systems including automobiles, ships, automatic driving in 
traffic, etc., intelligent technologies. Develop service robots, special-purpose robots, etc., 
core technologies and support unmanned system application and manufacturing 
development.  
 
Intelligent virtual reality modeling technology. Key breakthroughs in intelligent modeling 
technology for virtual counterparts. Increasing the sociality, diversity, and lifelike quality of 
virtual reality intelligent counterpart behavior. Realize the organic integration, high 
efficiency, and interactivity of virtual reality and augmented reality, etc., technologies. 
 
Intelligent computing chips and systems. Key breakthroughs in high energy 
efficiency, reconfigurable brain-inspired computing chips and brain-inspired visual sensor 
systems with computational imaging capabilities. Research and develop high-efficiency 
brain-inspired neural network architectures and hardware systems with autonomous 
learning capabilities. Realize brain-inspired intelligent systems with multimedia sensory 
information understanding, intelligence growth, and common sense reasoning capabilities. 
 
Natural language processing technology. Key breakthroughs in natural language grammar 
logic, word-concept symbols, and deep semantic analysis core technologies. Advance 
effective human-machine communication and free interaction. Realize multi-style, multi-
language, multi-domain natural language intelligent understanding and automated 
[results] generation. 
 
 
 


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Box 2: Key General Technologies 
 
1. Knowledge computing engines and knowledge service technology. Researching 
knowledge computing and visual interaction engines; researching innovative 
design, digital creation, and commercial intelligence with visual media at the core; 
developing large-scale organic data knowledge discovery. 
2. Cross-medium analytic reasoning technology. Researching cross-medium unified 
indicators, connected understanding and knowledge mining, knowledge map 
building and learning, knowledge evolution and inference, intelligent description 
and generation, etc., technology; developing cross-medium analytic reasoning 
engine and verification systems. 
3. Key swarm intelligence technology. Developing swarm intelligence's active 
perception and discovery, knowledge gain and generation, cooperation and sharing, 
evaluation and evolution, human-machine integration and enhancement, self-
preservation and mutual security, etc., key technology studies; building service 
system architecture for the crowd intelligence space; researching mobile crowd 
intelligent coordinated decision making and control technologies. 
4. Hybrid enhanced intelligent new architectures and technologies. Researching hybrid 
enhanced intelligent core technology and cognitive computing frameworks; new-
model hybrid computing architectures, human-machine collective driving, online 
intelligent learning technology, and hybrid enhanced frameworks for simultaneous 
management and control. 
5. Autonomous unmanned systems intelligent technology. Researching unmanned 
autonomous control intelligent technology for automobiles, ships, traffic, automatic 
driving, etc.; service, space, maritime, and polar robot technology; unmanned 
workshop/intelligent factory intelligent technology; high-end intelligent control 
technology and autonomous unmanned operating systems. Researching 
positioning, navigation, recognition, etc., robotic and mechanical arm autonomous 
control technology for visual sensing in complex environments.  
6. Virtual reality intelligent modeling technology. Researching mathematical 
expression and modeling methods for virtual counterpart intelligent behavior; 
problems such as natural, persistent, and deep exchange between users and virtual 
counterparts and virtual environments; intelligent counterpart modeling technology 
and method systems. 
7. Intelligent computing chips and systems. Researching neural network processors, 
as well as high-energy efficiency, reconfigurable brain-inspired computing chips, 
etc.; new-model perception chips and systems, intelligent computing system 
structure and systems, and AI operating systems. Researching architectures 
suitable for AI hybrid architectures, etc. 
8. Natural language processing technology. Researching short text computing and 
analysis technology, cross-language text mining technology and turning toward 
semantic comprehension technology for machine cognitive intelligence, and 
human-machine interaction systems for multimedia information comprehension. 
 
 
 
 
 
 


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3. Coordinate the layout of AI innovation platforms 
  
Construct AI innovation platforms. Strengthen the foundational support for AI research and 
development and applications. AI open-source hardware and software infrastructure 
platforms should focus on building and supporting unified computing frameworks for 
knowledge reasoning, probability statistics, depth learning, and other AI paradigms. Form 
and promote an ecological chain of platforms for interaction and synergies among AI 
software, hardware, and intelligent clouds. The group intelligent service platform should 
focus on the construction of knowledge resource management and the open sharing tools 
based on the large-scale cooperation on the Internet. Create a platform and service 
environment for the innovation of the industry and university. The hybrid enhanced 
intelligent support platforms should focus on the construction of a heterogeneous real-
time computing engine supporting large-scale training and a new computing clusters, 
providing a service-oriented, systematic platform and solution for complex intelligent 
computing. Autonomous unmanned system support platform focuses on the construction 
of autonomous system environmental awareness, autonomous collaborative control, 
intelligent decision-making and other AI common core technology support systems. Create 
development and test environments for open, modular, reconfigurable autonomous 
unmanned systems. AI basic data and security detection platforms should focus on the 
construction of AI for the public data resource library, the standard test data set, cloud 
service platform, the formation of AI algorithms and platform security test evaluation 
methods, techniques, norms and tools, promoting the open sourcing and openness of all 
kinds of common software and technology platform. Promote military-civilian sharing and 
joint use for all kinds of platforms in accordance with the requirements of deep military-
civil integration related provisions. 
 
Box 3: Basic Support Platforms 
 
1. AI Open-Source Hardware and Software Infrastructure and Platforms. Establish big 
data and AI open-source software platforms, terminal, and cloud collaborative AI 
cloud service platforms, new multi-intelligent sensor and integrated platforms, new 
product design platforms based on AI hardware, and future network, big data 
intelligent service platforms. 
2. Group Intelligent Service Platforms. Establish group knowledge-based computing 
and support platforms, science and technology public service systems, group 
intelligent software development and verification automation systems, group 
intelligent software learning and innovation systems, open environment cluster 
decision-making systems, and group-sharing economic service systems. 
3. Hybrid Enhanced Intelligent Support Platforms. Establish AI supercomputing 
centers, large-scale super intelligent computing support environments, online 
intelligent education platforms, “human-in-the-loop” driving brains, intelligent 
platforms for complexity analyses and risk assessment in industrial development, 
intelligent security platforms to support nuclear power security operations, and 
research and development and testing platforms for human-machine joint driving 
technology. 
4. Autonomous Unmanned System Support Platforms. Establish common core 
technology and support platforms, independent unmanned systems, independent 
control of unmanned aerial vehicles, and automatic driving support platforms for 
auto, ship and rail traffic, service robots, space robots, marine robots, polar robot 


14 
support platforms, technical support platforms for intelligent factory and intelligent 
control equipment, etc. 
5. AI Basic Data and Security Detection Platforms. Construct artificial data-oriented 
public data resource libraries, standard test data sets, and cloud service platforms. 
Establish test models and evaluation models for the security of AI algorithms and 
platforms. Research and develop security evaluation tools for AI algorithms and 
platforms. 
4. Accelerate the training and gathering of high-end AI talent 
Make the construction of a high-end talent team of the utmost importance in the 
development of AI. Adhere to the combination of training and introduction. Improve the AI 
education system, strengthen the construction of a talent pool and echelons, especially 
accelerate the introduction of the world’s top talent and young talent, forming China’s AI 
top talent base. 
Cultivate high-level of AI innovative talents and teams. Support and cultivate the 
development potential of leading AI talent. Strengthen professional and technical 
personnel training for basic research, applied research, operations and maintenance 
aspects of AI. Pay attention to the training of compound talents, focusing on cultivating 
vertical composite talents for AI theory, methods, technology, products, and application, 
and compound talents who master the “AI +” economy, society, management, standards, 
law, and other horizontal areas. Through major research and development tasks and base 
and platform construction, converge high-end talents in AI. Create high-level innovation 
teams in a number of AI key domains. Encourage and guide domestic innovative talents 
and the teams to strengthen cooperation with the world’s top AI research institutions. 
 
Increase the introduction of high-end AI talent. Open up specialized channels and 
implement special policies to achieve the precise introduction of peak AI talent. Focus on 
the introduction of international top scientists and high-level innovation teams in neural 
awareness, machine learning, automatic driving, intelligent robots, and other areas. 
Encourage the use of flexible introduction of AI talent through project cooperation, 
technical advice, etc. Coordinate the use of the “Thousands Talents” plan and other 
existing talent plans to strengthen the field of AI talents, especially through the 
introduction of outstanding young talent. Improve enterprise human capital cost 
accounting and related policies. Encourage enterprises and scientific research institutions 
to introduce AI talent. 
 
Construct an AI academic discipline. Improve the disciplinary layout of the AI domain. 
Establish AI majors. Promote the construction of a discipline in the domain of AI. Establish 
AI institutes as soon as possible in pilot institutions. Increase the enrollment places for 
masters and PhDs in working in AI and related disciplines. Encourage colleges and 
universities to broaden the content of AI professional education on an original basis. Create 
a new model of “AI + X” compound professional training, attaching importance to cross-
integration of professional education for AI and mathematics, computer science, physics, 
biology, psychology, sociology, law, and other disciplines. Strengthen cooperation in 
production and research. Encourage universities, research institutes, enterprises and other 
institutions to carry out the construction of an AI discipline. 
 


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(2) Fostering a high-end, highly efficient smart economy 
 
Accelerate the fostering of an AI industry with a major leading and driving effect, stimulate 
the profound convergence of AI and all industrial areas, and create data-driven smart 
economic patterns with human-machine coordination, cross-sectoral convergence, and 
joint creation and sharing. Data and knowledge will become the first factor for economic 
growth; human-machine coordination will become the mainstream method of production 
and service; cross-sectoral convergence will become an important economic model; joint 
creation and sharing will become basic characteristics of the economic ecology; 
individualized demands and made-to-order will become new consumption trends; and 
productivity will increase substantially, drive industries to migrate towards the high end of 
value chains, powerfully support the development of the real economy, and 
comprehensively increase the quality and efficiency of economic development. 
 
1. Forcefully develop new AI industries 
 
Accelerate the transformation and application of key AI technologies, stimulate the 
integration of technologies with commercial model innovation, promote the innovation of 
smart products in focus areas, vigorously foster new AI business models, compose high-
end industry chains, and forge AI industry groups with international competitiveness. 
 
Smart software and hardware. Develop operating systems, databases, intermediary 
devices, development tools, and other such key software and hardware aimed at AI; make 
breakthroughs in graphic processing and other such core hardware; research solution 
plans for smart systems in pattern recognition, voice understanding, machine translation, 
smart interaction, knowledge processing, control and decision-making, etc.; and foster 
and expand basic software and hardware industries aimed at AI. 
 
Smart robots. Tackle core components and special sensors for smart robots, perfect 
hardware interface standards, software interface standards, and safe usage standards for 
smart robots. Research and develop smart industrial robots and smart service robots, 
realize large-scale application, and enter into global markets. Research, produce, and 
popularize space robots, maritime robots, polar robots, and other such special kinds of 
smart robots. Establish smart robot standard systems and security norms. 
 
Smart delivery tools. Develop self-driving vehicles and rail traffic systems; strengthen the 
integration and coordination of vehicle load sensing, automatic driving, the Internet of cars, 
the Internet of Things, and other such technologies; develop smart traffic sensing systems, 
create national indigenous automatic driving platform technology systems and industrial 
assembly capabilities; and explore self-driving vehicle sharing models. Develop consumer 
and commercial unmanned aircraft and unmanned ships, and establish and trial 
specialized service systems for authentication, monitoring, technology competition, etc., 
perfect management measures for the space and maritime areas. 
 
Virtual reality and augmented reality. Make breakthroughs in key technologies such as 
high-performance software modelling, content capturing and generation, augmented 
reality and human-machine interaction, integrated environments and tools, etc. Research 
and create virtual display devices, optical devices, high-performance three-dimensional 
display devices, development engines, and other such products. Establish standards and 


16 
evaluation systems for virtual reality and augmented reality technologies, products, and 
services, and promote their converged application in focus sectors. 
 
Smart terminals. Accelerate the research and development of smart terminal core 
technologies and products, develop new-generation smart phones, on-board smart 
terminals for cars, and other such mobile smart terminal products and equipment. 
Encourage the research and development of smart watches, smart earpieces, smart 
glasses, and other such wearable terminal products, and expand product forms and 
application services. 
 
Basic Internet of Things devices. Develop high-sensitivity and highly reliable smart sensors 
and chips supporting the new-generation Internet of Things. Make progress in core Internet 
of Things technologies such as RFID and short-distance machine communications, as well 
as key components such as low-power processors. 
 
2. Accelerate and promote the upgrade of industrial intelligentization 
 
Promote the converged innovation of AI in all sectors. Launch AI application 
demonstrations and trials in focus sectors and areas such as manufacturing, agriculture, 
logistics, finance, commerce, household goods, etc. Promote the application of AI at scale, 
and comprehensively upgrade the smartness level of industrial development. 
 
Smart manufacturing. Focus on the major demands for building a strong manufacturing 
country, move forward the integrated application of systems such as key technologies and 
equipment for smart manufacturing, core supporting software, the industrial internet, etc. 
Research and develop smart products and smart connected products, tools and systems 
that can be used in smart manufacturing, and smart manufacturing cloud service 
platforms. Popularize smart manufacturing processes, distributed smart manufacturing, 
networked coordinated manufacturing, long-distance diagnosis and operational services, 
and other such novel manufacturing models. Establish smart manufacturing standard 
systems, and move forward with the intelligentization of manufacturing activities across 
the entire lifecycle. 
 
Smart agriculture. Research and formulate smart agricultural sensing and control systems, 
smart agricultural equipment, autonomous tasking systems for farming equipment across 
fields, etc. Establish and complete smart agriculture information remote sensing and 
monitoring networks integrating air, space, and land components. Establish model 
agriculture big data smart decision-making and analysis systems, launch trials of smart 
farms, smart plant factories, smart pastures, smart fisheries, smart orchards, smart farm 
produce processing workshops, green and smart farm product supply chains and other 
such integrated applications. 
 
Smart logistics. Strengthen research, development and broad use of smart logistics 
equipment for smart loading, unloading, and transportation; parcel sorting, processing and 
delivery; etc. Establish smart deep-sensing storage systems, and enhance storage and 
operational management levels and efficiency. Perfect smart logistics public information 
platforms and command systems, product quality authentication and tracing systems, 
smart distribution and dispatch systems, etc. 
 


17 
Smart finance. Establish big data systems for finance, and enhance multimedia data 
processing and comprehension capabilities for finance. Innovate smart financial products 
and services, develop new financial business models. Encourage the financial sector to use 
smart customer service, smart inspection, and other such technologies and equipment. 
Build smart warning and prevention systems for financial risk. 
 
Smart commerce. Encourage the application of cross-media analysis and reasoning, 
knowledge computing engines and knowledge services, and other such new technologies 
in the commercial area, and popularize AI-based novel commercial services and decision-
making systems. Build cross-medium data platforms covering geographic positioning, 
online media, urban basic data, etc., and support enterprises' launching smart services. 
Encourage the provision of made-to-order commercial smart decision-making services 
focusing on individual demands and enterprise management. 
 
Smart household goods. Strengthen the converged application of AI technology and 
household and building systems, and enhance the smartness levels of building facilities 
and household goods. Research, develop, and use household connection and interactivity 
agreements, as well as interface standards suited for different application settings. 
Enhance sensing and connection capabilities of household electrical appliances, durable 
goods and other such household products. Support smart household enterprises in 
innovating new service models, and promote interactive and sharing solutions and plans. 
 
3. Forcefully develop smart enterprises 
 
Promote the upgrading of enterprises' smartness levels on a large scale. Support and guide 
enterprises to use new AI technologies in core operational segments such as design, 
production, management, logistics, sales, etc. Build novel enterprise organization 
structures and operational models; create smart and converged business models for 
manufacturing, services, and finance; and develop individualized made-to-order; and 
broaden smart product supply. Encourage large-scale Internet enterprises to build cloud 
manufacturing platforms and service platforms, and provide online key industry software 
and model databases aimed at manufacturing enterprises. Launch outsourcing services for 
manufacturing capacity, and promote the development of smartness among small and 
mid-size enterprises. 
 
Popularize the use of smart factories. Strengthen the application and demonstration of key 
technologies and system methods for smart factories. Focus on popularizing production 
line reconstruction and dynamic smart control, production faculty smart interconnection 
and cloud data collection, multi-dimensional human-machine-object coordination, 
interoperability, and other such technologies. Encourage and guide enterprises to build 
factory big data systems, networked distributed production facilities, etc. Realize the 
networking of production equipment, the visualization of production data, the transparency 
of production processes, and the automation of production sites; and enhance the 
smartness levels of factory operational management. 
 
Accelerate the fostering of AI industry-leading enterprises. Accelerate the creation of 
global leading AI enterprises and brands in advantageous areas such as unmanned aircraft, 
speech recognition, pattern recognition, etc. Accelerate the fostering of a batch of key 
enterprises in novel areas such as smart robots, smart cars, wearable equipment, virtual 
reality, etc. Support AI enterprises to strengthen their patent structures, and take the lead 


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in or participate in the formulation of international standards. Promote domestic 
advantageous enterprises, sectoral organizations, scientific research bodies, higher 
education institutes, etc., to jointly establish the AI Industry and Technology Innovation 
Alliance of China. Support key backbone enterprises to build open source hardware 
factories, open source software platforms, create innovative ecologies integrating all kinds 
of resources, stimulate small and mid-size AI enterprises to develop and to be used in all 
areas. Support all kinds of bodies and platforms to provide specialized services aimed at AI 
enterprises. 
4. Create AI innovation heights 
Combined with each locality’s foundation and advantages, according to the field of AI 
applications classifications, advance the layout of the relevant industries. Encourage local 
industry chains and innovation chains around AI. Gather high-end factors, high-end 
enterprises, and high-end talent. Build AI industry clusters and heights of innovation. 
 
Launch AI innovation application pilot demonstrations. In areas where the AI foundation is 
favorable and its development potential bigger, organize and launch national AI innovation 
experiments. Explore systems and mechanisms, policy and regulation, the cultivation of 
talent, and other major reforms. Promote the transformation of the AI achievements, major 
product integrated innovation, and demonstration of applications. Form replicable, 
promotable experience, leading to the promotion of intelligent economy and intelligent 
social development. 
 
Construct national AI industrial parks. Rely upon national independent innovation 
demonstration areas and the national high-tech industry development zone and other 
innovative vectors. Strengthen science and technology talent, finance, policy, and other 
elements of the optimal allocation and combination. Accelerate the construction of AI 
industry innovation cluster. 
 
Construct national AI mass innovation bases. Relying on colleges and universities and 
scientific research institutes concentrated in localities, build AI field professionalized 
innovation platforms and other new entrepreneurial service agencies. Construct a number 
of low-cost, convenient, all-factor, open-style AI ‘hackerspaces.’ Improve incubation 
services system, promote the transformation of AI scientific and technological 
achievements, and support AI innovation and entrepreneurship. 
(3) Construct a safe and convenient intelligent society 
 
Based on the goal of improving people's living standards and quality, speed up and deepen 
the applications of AI, increase the level of intelligentization of the whole society to form an 
all-encompassing and ubiquitous intelligent environment. Increasingly, repetitive, 
dangerous tasks will be completed by AI, while individual creativity will play a greater role. 
Form more high-quality and high comfort jobs; make precision intelligent services more 
diverse, such that people can maximize their enjoyment of high quality services and 
convenient life. Through a substantial increase in the level of intelligentization of social 
governance, make social operations more safe and efficient. 


19 
1. Develop convenient and efficient intelligent services 
 
Accelerate the application of innovative AI throughout education, health care, pension and 
other urgent needs involving people's livelihood, to provide for the public personalized, 
diversified, high-quality services. 
 
Intelligent Education. Utilize intelligent technology to accelerate and promote a personnel 
training model and reform to teaching methods; establish new-type education systems, 
including intelligent learning and interactive learning. Launch the construction of 
intelligent campuses; promote AI in teaching, management, resource construction, and 
other full-scale applications. Develop three-dimensional integrated teaching field, based 
on big data intelligent online learning and education platforms. Develop intelligent 
educational assistants; establish intelligent, fast and comprehensive education analysis 
system. Establish a learner-centered educational environment, and provide precision-
deployed education services, achieve daily education and lifelong education. 
 
Intelligent Medical Care. Promote the use of new models and new methods of AI treatment, 
establish a rapid, accurate intelligent medical system. Explore intelligent hospital 
construction, develop human-machine coordinated surgical robots and intelligent clinic 
assistants. Pursue research and development on flexible wearable, biologically compatible 
physiological monitoring systems, research and development of human-computer 
collaboration intelligent clinical diagnosis and treatment programs. Achieve intelligent 
image recognition, pathology classification, and intelligent multi-disciplinary consultation. 
Carry out large-scale genome recognition, proteomics, metabolomics, and other research 
and development of new drugs based on AI, promote intelligent pharmaceutical regulation. 
Strengthen epidemic intelligence monitoring, prevention, and control. 
 
Intelligent Health and Elder Care Systems. Strengthen community intelligent health 
management, achieve breakthroughs in big data analysis, Internet of Things, and other key 
technologies. Research and develop health management wearable equipment and home 
intelligent health testing and monitoring equipment. Promote changes in health 
management from point-like monitoring to continuous monitoring, from short process 
management to long process management. Construct intelligent elder care communities 
and institutions; build a safe and convenient intelligent pension infrastructure system. 
Strengthen the intelligentization of products for elderly persons and intelligent products 
suitable for the aged. Develop audio-visual aid equipment, physical auxiliary equipment, 
and other intelligent home care equipment, expanding the elderly’s activity space. Develop 
mobile social and service platform for the elderly and emotional escort assistant to 
enhance the quality of life of the elderly. 
2. Promote the intelligentization of social governance 
 
Promote the application of AI technology for administrative management, judicial 
management, urban management, environmental protection, and other hot and difficult 
issues in social governance, to promote the modernization of social governance. 
 
Intelligent Government. Develop an AI platform for government services and decision-
making. Develop a decision-making engine for the open environment. Promote 
applications in research on complex social problems, policy assessment, risk warning, 


20 
emergency response, and other major matters of strategic decision-making. Strengthen 
the integration of government information resources and accurate forecasting of public 
demands, and smooth communication channels between the government and the public. 
 
Smart Courts. Construct a set of trial, personnel, data applications, judicial disclosure, and 
dynamic monitoring into an integrated court data platform. Promote AI applications for 
applications including evidence collection, case analysis, and legal document reading and 
analysis. Achieve the intelligentization of courts and trial systems and trial capacity.  
 
Smart Cities. Build an intelligentized city infrastructure, develop intelligent buildings, and 
promote the intelligentization, transformation, and upgrading of underground corridors and 
other municipal infrastructure. Construct urban big data platforms to build a 
heterogeneous, integrated data system for urban operations and management. Achieve 
comprehensive perception and deep understanding of the operation of complex urban 
systems for urban infrastructure and urban green space, wetlands, and other important 
ecological elements. Research and develop to build community public service information 
systems. Promote community service system and residents’ intelligent home system 
collaboration. Promote the intelligentization of the full lifecycle of urban planning, 
construction, and management. 
 
Smart Transportation. Research, establish, and operate vehicle automatic driving and road 
coordination technology systems. Research and develop information and integrated data 
platforms for transportation under complex multi-dimensional conditions. Establish 
intelligentized transportation command, control, and integrated operations. Actualize 
intelligent transportation obstacle removal and integrated management and coordination 
and command. Build intelligent transportation monitoring, management, and service 
systems covering the ground, tracks, low altitude, and the sea. 
 
Intelligent Environmental Protection. Establish an intelligent monitoring large data 
platforms and systems covering the atmosphere, water, soil, and other environmental 
areas. Build information-sharing and intelligent environmental monitoring networks and 
service platforms for coordination of land and sea, integration of atmosphere and earth, 
and upwards and downwards synergies. Research and develop intelligent forecasting 
models and method and early warning programs for energy resource consumption and 
environmental pollutant discharge. Strengthen the Beijing-Tianjin-Hebei, Yangtze River 
Economic Zone, and other major national strategic regions’ construction of intelligent 
prevention and control system for environmental protection and sudden environmental 
events. 
3. Use AI to enhance public safety and security capabilities 
Advance the deepening of AI applications in the field of public safety. Promote the 
construction of public safety and intelligent monitoring and early warning and control 
systems. Research and develop a variety of detection sensor technology, video image 
information analysis and identification technology, biometric identification technology, 
intelligent security and police products. Establish intelligent monitoring platform for 
comprehensive community management, new criminal investigations, anti-terrorism, and 
other urgent needs. Strengthen the upgrading and intelligentization of security equipment 
for key public areas. Support carrying out public security regional demonstrations based on 
AI according to the conditions of the community or the city. Strengthen the use of AI for 
food safety protection, food classification, warning level, food safety risks and assessment, 


21 
and the establishment of intelligent food safety early warning system. Strengthen the 
effective monitoring of natural disasters, natural disasters, around the earthquake disaster, 
geological disasters, meteorological disasters, floods and disasters and marine disasters 
and other major natural disasters, to build an intelligent monitoring and early warning and 
comprehensive response platform. 
4. Promote social interaction and mutual trust 
Give full play to the role of AI technology in enhancing social interaction and promoting 
credible communication. Strengthen the next generation of social network research and 
development, accelerate innovation in augmented reality, virtual reality, and other 
technologies to promote the integrative use of virtual environments and physical 
environments to meet personal perception, analysis, judgment and decision-making real-
time information needs, and to achieve the smooth transition of different scenes of work, 
study, life, and entertainment. In order to improve the interpersonal communication needs, 
develop intelligent assistant products with the ability to accurately understand the needs 
of emotional interaction. Promote the integration of blockchain technology and AI, 
establish a new social credit system, and minimize the cost and risks of interpersonal 
communication. 
(4) Strengthen military-civilian integration in the AI domain 
  
Deepen implementation of military-civilian integration development strategy, to promote 
the formation of an all-element, multi-field, high efficiency AI military-civilian integration 
pattern. Build new generation AI based on research and development in the common 
theory and critical common technology. Establish mechanisms to normalize 
communication and coordination among scientific research institutes, universities, 
enterprises and military industry units. Promote military-civilian two-way transformation of 
AI technology. Strengthen a new generation of AI technology as a strong support to 
command and decision-making, military deduction, defense equipment, and other 
applications. Guide defense domain AI technology toward civilian applications. Encourage 
and advantage people’s scientific research forces to participate in the domain of national 
defense for major scientific and technological innovation tasks in AI. Promote all kinds of AI 
technology to become quickly embedded in the field of national defense innovation. 
Strengthen the construction of military and civilian AI technology standard systems.  
Promote the overall layout and open sharing of science and technology innovation 
platforms and bases. 
(5) Build a safe and efficient intelligent infrastructure system 
 
Vigorously promote the construction of intelligent information infrastructure. Enhance the 
traditional level of intelligent infrastructure to form a smart economy, intelligent society 
and national defense needs of the infrastructure system. Speed up the promotion of 
information transmission as the core of the digital, network information infrastructure. 
Take integration awareness, transmission, storage, computing, and processing in 
intelligent information infrastructure changes. Optimize network infrastructure, research 
and develop the layout of fifth generation mobile communication (5G) systems. Improve the 
Internet of Things infrastructure. Accelerate the integration of information network 
construction. Improve low-latency, high-throughput transmission capacity. Coordinate the 


22 
use of big data infrastructure, strengthen data security and privacy protection, to provide 
massive data support for AI research and development and extensive applications. Build 
high-performance computing infrastructure, and enhance the service support capabilities 
of supercomputing centers for AI applications. Construct distributed and efficient energy 
Internet, form multi-energy support complementary, timely, and effective access to new 
energy networks. Promote intelligent energy storage facilities, intelligent electricity 
facilities, energy supply and demand information to achieve real-time matching and 
intelligent response. 
  
  
Box 4: Intelligentized Infrastructure 
 
1.   Network Infrastructure. Speed up the layout of real-time collaborative AI 5G 
enhanced technology research and the development and application of space-
oriented collaborative AI for the construction of high-precision navigation and 
positioning networks to strengthen the core of intelligent sensing technology 
research and key facilities. Develop intelligent industrial support, driving networks, 
etc., to study the intelligent network security architecture. Speed up the 
construction of integrated information network for space and earth, promoting a 
space-based information network, the future of the Internet, mobile communication 
network of the full integration. 
2. 
   Big Data Infrastructure. Rely on a national data sharing exchange platform, open 
data platform and other public infrastructure. Construct governance, public 
services, industrial development, technology research and development, and other 
fields of big data information databases Support the implementation of national 
governance data applications. Integrate various types of social data platforms and 
data center resources. Create nationwide integrated service capabilities with 
reasonable layout and linkages. 
3. High-performance computing infrastructure. Continue to strengthen the 
supercomputing infrastructure, distributed computing infrastructure and cloud 
computing center construction. Build sustainable development of high-
performance computing application for the ecological environment. Promote the 
next generation of supercomputer research and development and applications. 
(6) Plan a new generation of AI major science and technology 
projects 
 
For the development of China’s AI needs and weak links, establish of a new generation of AI 
major scientific and technological projects. Strengthen the overall co-ordination, clear the 
boundaries of the tasks and the focus of research and development. Form a new 
generation of AI major scientific and technological projects as the core, and use existing 
R&D layout to support the “1 + N” AI program. 
 
“1” refers to a new generation of AI scientific and technological mega-projects, focusing on 
forward-looking layout for basic theories and key common technologies, including the 
study of big data intelligence, cross-media perception and computing, hybrid enhanced 
intelligence, group intelligence, autonomous collaborative control, and decision-making 
theory. Research knowledge computing engines and knowledge service technologies, 
cross-medium analysis reasoning technology, key swarm intelligence technologies, new 


23 
architecture and new technology for hybrid enhanced intelligent, autonomous unmanned 
control technology, and basic theory and common technology for open-source shared AI. 
Continue to carry out the development of AI prediction and research, strengthening the 
economic and social impact of and countermeasures for AI. 
 
“N” refers to the national planning and deployment of AI research and development 
projects. Focusing on strengthening the new generation of AI with the convergence major 
scientific and technological projects, collaborative impetus for research, technological 
breakthroughs and product development applications. Strengthen the convergence of 
major national science and technology projects. Support AI hardware and software 
development in the “Hegaoji” Megaproject,1 integrated circuit equipment and other national 
science and technology major projects. Strengthen mutual support for AI and other 
“Technological Innovation 2030 - Mega-Projects.” Accelerate the use of AI to provide 
support for major technical breakthroughs in brain science and brain computing, quantum 
information and quantum computing, intelligent manufacturing and robotics, and big data 
research. The National Key Research and Development Plan will continue to promote high-
performance computing and other key special applications, while increasing support for AI-
related technology research and development and application; the National Natural 
Science Foundation will strengthen cross-disciplinary research and support for free 
exploration in the field of AI. Focus on special deployment and strengthen the application 
of AI technology demonstrations to the deep sea space station, health protection, and 
other major projects, smart cities, intelligent agricultural equipment and other Key National 
R&D Projects. Support the openness and sharing of research results on basic theory of AI 
and common technology through other basic science and technology plans. 
 
Innovate in the organization and implementation of models for new generation AI major 
scientific and technological projects. Adhere to focus on doing things, focusing on the 
principle of breakthrough. Give full play to the role of market mechanisms to mobilize 
departments, local, business and social forces to promote the implementation of all 
aspects. Pursue clear management responsibility, regular assessments, to strengthen the 
dynamic adjustments and improve management efficiency. 
IV.  Resource Allocation 
 
Fully use existing finances, bases and other such stored resources, comprehensively plan 
the allocation of international and domestic innovation resources, give rein to the guiding 
role of finance administration input and policy incentives, and the dominant role of the 
market in allocating resources, impel enterprises and society to expand input, and create a 
new pattern of multi-sided support through finance administration funding, financial 
capital, and social capital. 
                                               
 
1 Translator’s note: This refers to the Medium and Long-term Plan for S&T Development 
2006-2020 megaproject: core (he) electronic devices, high-end (gao) general-purpose 
chips, and basic (ji) software. 


24 
(1) Establish financial support mechanisms guided by the 
financial administration and dominated by the market 
 
Comprehensively plan multiple-channel financial input by government and markets, 
strengthen support through finance administration funding, enliven existing resources, and 
provide support for fundamental and advanced AI research, critical public technology 
breakthroughs, result transformation, base and platform construction, innovative 
application demonstrations, etc. Use existing policy input funds to support AI programs to 
meet conditions, encourage leading and backbone enterprises and industrial innovation 
alliances to take the lead in establishing marketized AI development bases. Use angel 
investment, risk investment, start-up investment funds, financial market funding and many 
other such channels to guide social capital to support AI development. Vigorously use 
governmental and social capital cooperation and other such models and guide social 
capital to participate in the implementation of major AI programmes and the 
transformation and application of scientific and technological achievements.  
(2) Optimize arrangements to build AI innovation bases 
According to the national-level science and technology innovation base arrangements and 
frameworks, comprehensively promote a few internationally advanced innovation bases in 
the area of AI construction. Guide existing AI-related national focus laboratories, corporate 
national focus laboratories, national engineering laboratories, and other such bases, and 
conduct research focused on an advanced direction of a new generation of AI. According to 
regulatory procedure, build technological and industrial innovation bases related to the AI 
area with enterprises in the lead, and in cooperation between industry, scholarship, and 
research. Give rein to the driving role of leading and backbone enterprises concerning 
technological innovation demonstrations. Develop specialized public maker spaces in the 
AI area, stimulate the precise linkage of the newest technological achievements, resources 
and services. Fully give rein to the role of all kinds of innovation bases in concentrating 
talent, finance, and other such innovation resources; make breakthroughs in basic and 
advanced AI theory and key common technologies; and launch application 
demonstrations. 
(3) Comprehensively plan international and domestic innovation 
resources 
  
Support domestic AI enterprises to cooperate with international leading AI schools, 
scientific research institutes and teams. Encourage domestic AI enterprises to "go out," 
and provide conveniences and services to powerful AI enterprises conducting foreign 
mergers or acquisitions, share investment, start-up investment, establishing foreign 
research centres, etc. Encourage foreign AI enterprises and research institutes to establish 
research and development centers in China. With the support of the “One Belt, One Road” 
strategy, promote the construction of international AI science and technology cooperation 
bases, joint research centres, etc.; accelerate the broad application of AI technologies in 
countries along the “One Belt, One Road.” Promote the establishment of international AI 
organizations, jointly formulate related international standards. Support related sectoral 
associations, alliances, and service bodies to build globalized service platforms aimed at AI 
enterprises. 


25 
V.  Guarantee Measures 
Aiming at the realistic requirements of promoting the healthy and rapid development of AI 
in China, it is necessary to deal with the possible challenges of AI, form an institutional 
arrangement to adapt to the development of AI, build an open and inclusive international 
environment, and reinforce the social foundation of AI development. 
(1) Develop laws, regulations, and ethical norms that promote the 
development of AI 
 
Strengthen research on legal, ethical, and social issues related to AI, and establish laws, 
regulations and ethical frameworks to ensure the healthy development of AI. Conduct 
research on legal issues such as civil and criminal responsibility confirmation, proteciton of 
privacy and property, and information security utilization related to AI applications. 
Establish a traceability and accountability system, and clarify the main body of AI and 
related rights, obligations, and responsibilities. Focus on autonomous driving, service 
robots, and other application subsectors with a comparatively good usage foundation, and 
speed up the study and development of relevant safety management laws and regulations, 
to lay a legal foundation for the rapid application of new technology. Launch research on AI 
behavior science and ethics and other issues, establish an ethical and moral multi-level 
judgment structure and human-computer collaboration ethical framework. Develop an 
ethical code of conduct and R&D design for AI products, strengthen the assessment of the 
potential hazards and benefits of AI, and build solutions for emergencies in complex AI 
scenarios. China will actively participate in global governance of AI, strengthen the study of 
major international common problems such as robot alienation and safety supervision, 
deepen international cooperation on AI laws and regulations, international rules and so on, 
and jointly cope with global challenges. 
(2) Improve key policies for the support of AI development 
 
Implement tax incentives for small and mid-sized enterprise and startup AI development, 
and, using high-tech enterprises, tax incentives, R&D cost deductions, and other policies, 
support the development of AI enterprises. Improve the implementation of open data and 
protection-related policies, launch open public data reform pilots to support the public and 
enterprises in fully tapping the commercial value of public data, and promote the 
application of AI innovation. China will study the policy system of education, medical care, 
insurance, and social assistance to adapt to AI, and effectively deal with the social 
problems brought by AI. 
(3) Establish an AI technology standards and intellectual property 
system 
 
Conduct research on strengthening the AI standards framework system.  Adhere to the 
principles of security, availability, interoperability, and traceability; and gradually establish 
and improve the basic basis of AI, interoperability, industry applications, network security, 
privacy protection, and other technical standards. Speed up the promotion of autonomous 
driving, service robot, and other application sector industry associations in developing 
relevant standards. Encourage AI enterprises to participate in or lead the development of 


26 
international standards, and a technical standards "going out" approach to promote AI 
products and services in overseas applications. Strengthen the protection of intellectual 
property in the field of AI, improve the field of AI technology innovation, patent protection, 
and standardization of interactive support mechanisms to promote the innovation of AI 
intellectual property rights. Establish AI public patent pools to promote the use of AI and 
the spread of new technologies. 
(4) Establish an AI security supervision and evaluation system 
 
Strengthen research and evaluation of the influence of AI on national security and secrecy 
protection; improve the security protection system of human, technology, material, and 
management support; and construct an early warning mechanism of AI security 
monitoring. Strengthen the development of AI technology prediction, research and follow-
up research, adhere to a problem-oriented, accurate grasping of technology and industry 
trends. Enhance the awareness of risk, pay attention to risk assessment and prevention 
and control, and strengthen prospective prevention and restraint guidance. In the near 
term focus on the impact on employment, with a long-term focus on the impact on social 
ethics, to ensure that the development of AI falls with the sphere of secure and 
controllable. Establish and improve an open and transparent AI supervision system, the 
implementation of design accountability, and application of the supervision of a two-tiered 
regulatory structure, to achieve management of the whole process of AI algorithm design, 
product development and results application. Promote AI industry and enterprise self-
discipline, and earnestly strengthen management, increase disciplinary efforts aimed at 
the abuse of data, violations of personal privacy, and actions contrary to moral ethics. 
Strengthen AI cybersecurity technology research and development, strengthen AI products 
and systems cybersecurity protection. Develop dynamic AI research and development 
evaluation mechanisms, focus on AI design, product and system complexity, risk, 
uncertainty, interpretability, potential economic impact, and other issues. Develop a 
systematic testing methods and indicators system. Construct a cross-domain AI test 
platform to promote AI security certification, and assessment of AI products and systems 
key performance. 
(5) Vigorously strengthen the training of an AI labor force 
 
Accelerate the study of the employment structure brought on by AI, changes in 
employment methods, and the skills demand of new occupations and jobs, establish a 
lifelong learning and employment training system to meet the needs of the intelligent 
economy and intelligent society, and support institutions of higher learning, vocational 
schools and socialization training Institutions to carry out AI skills training.  Substantially 
increase the professional skills of workers to meet the development requirements of 
China's AI to bring high-quality jobs. Encourage enterprises and organizations to provide AI 
skills training for employees. Strengthen the re-employment training and guidance of 
workers to ensure the smooth transfer of simple and repetitive workers due to AI. 
(6) Carry out a wide range of AI scientific activities 
  
Support the development of a variety of AI scientific activities, encourage the broad masses 
of scientific and technological workers to join the promotion of AI popular science, and 


27 
comprehensively improve the level of the whole society on the application of AI. Implement 
a universal intelligence education project. In the primary and secondary schools, set up AI-
related courses, and gradually promote programming education to encourage social forces 
to participate in the promotion and development of educational programming software and 
games. Construct and improve the AI science infrastructure, give full play to all kinds of AI 
innovation base platforms and other popular science roles, encourage AI enterprises, and 
research institutions to build open source platforms for public open AI research and 
development, plus production facilities or exhibition halls. Support the development of AI 
competitions, encourage the formation of a variety of AI science creational work efforts. 
Encourage scientists to participate in AI science. 
VI.  Organization and Implementation 
 
The development plan for a new generation of AI is a far-sighted scheme affecting the 
overall picture and the long term. We must strengthen organizational leadership, complete 
mechanisms, take aim at objectives, keep tasks closely in view, realistically grasp 
implementation with a spirit of hammering nails, and carry out the blueprint to the end. 
(1) Organizational leadership 
 
According to the unified deployment of the Party Center and the State Council, the National 
Science and Technology Structural Reform and Innovation System Construction Leading 
Small Group will take the lead in comprehensive planning and coordination, it will 
deliberate major tasks, major policies, major questions, and major work arrangements. 
Promote AI-related legal and regulatory construction. Guide, coordinate and supervise 
relevant departments in carrying out the deployment and implementation of tasks from the 
plan. With the support of the interministerial joint conferences for national science and 
technology planning (earmarks, funding, etc.) management, the Ministry of Science and 
Technology will, together with relevant departments, be responsible for moving forward the 
implementation of major science and technology programmes for a new generation of AI, 
and strengthen linkages and coordination with other programmatic tasks. Establish an AI 
Plan Implementation Office. This office will be part of the Ministry of Science and 
Technology and will be concretely responsible for moving the implementation of the plan 
forward. Establish an AI Strategy Advisory Committee, to research major far-sighted and 
strategic questions concerning AI and to provide advice and assessment concerning major 
policy decisions on AI. Move forward with the construction of an AI think tank, support all 
kinds of think tanks to launch research on major AI questions, and provide strong and 
powerful support for the development of AI. 
(2) Guarantee implementation 
 
Strengthen the deconstruction of plan tasks, clarify responsible work units, schedules and 
arrangements, formulate annual and phase-type implementation plans. Establish 
monitoring and evaluation mechanisms for the implementation situation of the plan, such 
as annual assessment and intermediate evaluation. Adapt to the characteristics of the 
rapid development of AI, and strengthen dynamic adjustment of plans and programs on the 


28 
basis of the progress of tasks, the completion of intermediate objectives, new trends in 
technological development, etc. 
(3) Trials and demonstrations 
 
We must formulate concrete plans for major AI tasks and focus policy measures, and 
launch trials and demonstrations. Strengthen comprehensive guidance over trials and 
demonstrations in all departments and all localities, quickly summarize and disseminate 
replicable experiences and methods. Advance the healthy and orderly development of AI 
through advance trials and guiding demonstrations. 
(4) Public opinion guidance 
 
Fully use all kinds of traditional media and new media to quickly propagate new progress 
and new achievements in AI, to let the healthy development of AI become a consensus in 
all of society, and muster the vigor of all of society to participate in and support the 
development of AI. Conduct timely public opinion guidance, and respond even better to 
social, theoretical, and legal challenges that may be brought about by the development of 
AI. 
  
###

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Index Report 2021
CHAPTER 7: 
AI Policy and  
National Strategies
Artificial Intelligence
Index Report 2021


2
CHAPTER 7 PREVIEW
Artificial Intelligence
Index Report 2021
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
Overview	
3
Chapter Highlights	
4
7.1 
NATIONAL AND REGIONAL 	
   
     AI STRATEGIES	
5
Published Strategies	
6
	
2017	
6
	
2018	
7
	
2019	
9
	
2020	
11
Strategies in Development 	
   
(as of December 2020)	
12
	
Strategies in Public Consultation	
12
Strategies Announced	
13
Highlight: National AI Strategies  
and Human Rights	
14
7.2 INTERNATIONAL 
     COLLABORATION ON AI	
15
Intergovernmental Initiatives	
15
	
Working Group	
15
	
Summits and Meetings	
16
Bilateral Agreements	
16
7.3 U.S. PUBLIC INVESTMENT IN AI	
17
Federal Budget for Non-Defense AI R&D	 17
U.S. Department of Defense  
Budget Request	
18
U.S. Government Contract Spending	
19
	
Total Contract Spending	
19
	
Contract Spending by  
	
Department and Agency	
19
7.4 AI AND POLICYMAKING	
21
Legislation Records on AI	
21
	
U.S. Congressional Record	
22
	
Mentions of AI and ML in  
	
Congressional/Parliamentary 
	
Proceedings	
22
Central Banks	
24
U.S. AI Policy Papers	
26
APPENDIX	
27
Chapter Preview
CHAPTER 7:
ACCESS THE PUBLIC DATA


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CHAPTER 7 PREVIEW
Artificial Intelligence
Index Report 2021
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
Overview
OVERVIEW
AI is set to shape global competitiveness over the coming decades, promising 
to grant early adopters a significant economic and strategic advantage. To 
date, national governments and regional and intergovernmental organizations 
have raced to put in place AI-targeted policies to maximize the promise of the 
technology while also addressing its social and ethical implications. 
This chapter navigates the landscape of AI policymaking and tracks efforts taking 
place on the local, national, and international levels to help promote and govern AI 
technologies. It begins with an overview of national and regional AI strategies and 
then reviews activities on the intergovernmental level. The chapter then takes a 
closer look at public investment in AI in the United States as well as how legislative 
bodies, central banks, and nongovernmental organizations are responding to the 
growing need to institute a policy framework for AI technologies.


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CHAPTER 7:
AI POLICY AND  
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CHAPTER
HIGHLIGHTS
CHAPTER HIGHLIGHTS
• 
Since Canada published the world’s first national AI strategy in 2017, more than 30 other 
countries and regions have published similar documents as of December 2020.
• 
The launch of the Global Partnership on AI (GPAI) and Organisation for Economic  
Co-operation and Development (OECD) AI Policy Observatory and Network of Experts  
on AI in 2020 promoted intergovernmental efforts to work together to support the 
development of AI for all.
• 
In the United States, the 116th Congress was the most AI-focused congressional session in 
history. The number of mentions of AI by this Congress in legislation, committee reports, and 
Congressional Research Service (CRS) reports is more than triple that of the 115th Congress. 


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Index Report 2021
To guide and foster the development of AI, countries and regions around the world are establishing strategies and 
initiatives to coordinate governmental and intergovernmental efforts. Since Canada published the world’s first national 
AI strategy in 2017, more than 30 other countries and regions have published similar documents as of December 2020. 
7.1 NATIONAL AND REGIONAL 
AI STRATEGIES
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
This section presents an overview of select national and regional AI strategies from around the world, including details on 
the strategies for G20 countries, Estonia, and Singapore as well as links to strategy documents for many others. Sources 
include websites of national or regional governments, the OECD AI Policy Observatory (OECD.AI), and news coverage. “AI 
strategy” is defined as a policy document that communicates the objective of supporting the development of AI while also 
maximizing the benefits of AI for society. Excluded are broader innovation or digital strategy documents which do not focus 
predominantly on AI, such as Brazil’s E-Digital Strategy and Japan’s Integrated Innovation Strategy.
COUNTRIES  
WITH PUBLISHED 
AI STRATEGIES: 32
COUNTRIES  
DEVELOPING  
AI STRATEGIES: 22 


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Published Strategies
2017
Canada
• 
AI Strategy: Pan Canadian AI Strategy
• 
Responsible Organization: Canadian Institute for 
Advanced Research (CIFAR) 
• 
Highlights: The Canadian strategy emphasizes 
developing Canada’s future AI workforce, supporting major 
AI innovation hubs and scientific research, and positioning 
the country as a thought leader in the economic, ethical, 
policy, and legal implications of artificial intelligence. 
• 
Funding (December 2020 conversion rate): CAD 125 
million (USD 97 million)
•  In November 2020, CIFAR published its most recent 
annual report, titled “AICAN,” which tracks progress on 
implementing its national strategy, which highlighted 
substantial growth in Canada’s AI ecosystem, as well 
as research and activities related to healthcare and AI’s 
impact on society, among other outcomes of the strategy.  
China
• 
AI Strategy: A Next Generation Artificial Intelligence 
Development Plan
• 
Responsible Organization: State Council for the People’s 
Republic of China
• 
Highlights: China’s AI strategy is one of the most 
comprehensive in the world. It encompasses areas 
including R&D and talent development through 
education and skills acquisition, as well as ethical norms 
and implications for national security. It sets specific 
targets, including bringing the AI industry in line with 
competitors by 2020; becoming the global leader in fields 
such as unmanned aerial vehicles (UAVs), voice and 
image recognition, and others by 2025; and emerging as 
the primary center for AI innovation by 2030.
• 
Funding: N/A
• 
Recent Updates: China established a New Generation 
AI Innovation and Development Zone in February 2019 
and released the “Beijing AI Principles” in May 2019 with 
a multi-stakeholder coalition consisting of academic 
institutions and private-sector players such as Tencent 
and Baidu. 
Japan
• 
AI Strategy: Artificial Intelligence Technology Strategy
• 
Responsible Organization: Strategic Council for AI 
Technology
• 
Highlights: The strategy lays out three discrete phases of 
AI development. The first phase focuses on the utilization 
of data and AI in related service industries, the second 
on the public use of AI and the expansion of service 
industries, and the third on creating an overarching 
ecosystem where the various domains are merged. 
• 
Funding: N/A
• 
Recent Updates: In 2019, the Integrated Innovation 
Strategy Promotion Council launched another AI strategy, 
aimed at taking the next step forward in overcoming 
issues faced by Japan and making use of the country’s 
strengths to open up future opportunities.
Others
Finland: Finland’s Age of Artificial Intelligence
United Arab Emirates: UAE Strategy for Artificial 
Intelligence
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Published Strategies
2018
European Union 
• 
AI Strategy: Coordinated Plan on Artificial Intelligence
• 
Responsible Organization: European Commission 
• 
Highlights: This strategy document outlines the 
commitments and actions agreed on by EU member 
states, Norway, and Switzerland to increase investment 
and build their AI talent pipeline. It emphasizes the value 
of public-private partnerships, creating European data 
spaces, and developing ethics principles.
• 
Funding (December 2020 conversation rate): At least 
EUR 1 billion (USD 1.1 billion) per year for AI research and 
at least EUR 4.9 billion (USD 5.4 billion) for other aspects 
of the strategy
• 
Recent updates: A first draft of the ethics guidelines was 
released in June 2018, followed by an updated version in 
April 2019.
France
• 
AI Strategy: AI for Humanity: French Strategy for Artificial 
Intelligence
• 
Responsible Organizations: Ministry for Higher 
Education, Research and Innovation; Ministry of Economy 
and Finance; Directorate General for Enterprises; Public 
Health Ministry; Ministry of the Armed Forces; National 
Research Institute for Digital Sciences; Interministerial 
Director of the Digital Technology and the Information 
and Communication System 
• 
Highlights: The main themes include developing 
an aggressive data policy for big data; targeting four 
strategic sectors, namely health care, environment, 
transport, and defense; boosting French efforts in 
research and development; planning for the impact of AI 
on the workforce; and ensuring inclusivity and diversity 
within the field.
• 
Funding (December 2020 conversion rate): EUR 1.5 
billion (USD 1.8 billion) up to 2022
• 
Recent Updates: The French National Research Institute 
for Digital Sciences (Inria) has committed to playing a 
central role in coordinating the national AI strategy and 
will report annually on its progress.
Germany
• 
AI Strategy: AI Made in Germany
• 
Responsible Organizations: Federal Ministry of 
Education and Research; Federal Ministry for Economic 
Affairs and Energy; Federal Ministry of Labour and Social 
Affairs
• 
Highlights: The focus of the strategy is on cementing 
Germany as a research powerhouse and strengthening 
the value of its industries. There is also an emphasis 
on the public interest and working to better the lives of 
people and the environment.
• 
Funding (December 2020 conversion rate): EUR 500 
million (USD 608 million) in the 2019 budget and EUR 
3 billion (USD 3.6 billion) for the implementation up to 
2025
• 
Recent Updates: In November 2019, the government 
published an interim progress report on the Germany AI 
strategy.
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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2018 (continued)
India
• 
AI Strategy: National Strategy on  
Artificial Intelligence: #AIforAll
• 
Responsible Organization: National Institution for 
Transforming India (NITI Ayog)
• 
Highlights: The Indian strategy focuses on both 
economic growth and ways to leverage AI to increase 
social inclusion, while also promoting research to 
address important issues such as ethics, bias, and 
privacy related to AI. The strategy emphasizes sectors 
such as agriculture, health, and education, where public 
investment and government initiative are necessary.
• 
Funding (December 2020 conversion rate): INR 7000 
crore (USD 949 million)
• 
Recent Updates: In 2019, the Ministry of Electronics and 
Information Technology released its own proposal to 
set up a national AI program with an allocated INR 400 
crore (USD 54 million). The Indian government formed 
a committee in late 2019 to push for an organized AI 
policy and establish the precise functions of government 
agencies to further India’s AI mission.
Mexico
• 
AI Strategy: Artificial Intelligence Agenda MX  
(2019 agenda-in-brief version)
• 
Responsible Organization: IA2030Mx, Economía
• 
Highlights: As Latin America’s first strategy, the Mexican 
strategy focuses on developing a strong governance 
framework, mapping the needs of AI in various industries, 
and identifying governmental best practices with an 
emphasis on developing Mexico’s AI leadership.
• 
Funding: N/A
• 
Recent Updates: According to the Inter-American 
Development Bank’s recent fAIr LAC report, Mexico is in 
the process of establishing concrete AI policies to further 
implementation.
United Kingdom
• 
AI Strategy: Industrial Strategy: Artificial Intelligence 
Sector Deal
• 
Responsible Organization: Office for Artificial 
Intelligence (OAI)
• 
Highlights: The U.K. strategy emphasizes a strong 
partnership between business, academia, and the 
government and identifies five foundations for a 
successful industrial strategy: becoming the world’s most 
innovative economy, creating jobs and better earnings 
potential, infrastructure upgrades, favorable business 
conditions, and building prosperous communities 
throughout the country. 
• 
Funding (December 2020 conversion rate): GBP 950 
million (USD 1.3 billion)
• 
Recent Updates: Between 2017 and 2019, the U.K.’s 
Select Committee on AI released an annual report on the 
country’s progress. In November 2020, the government 
announced a major increase in defense spending of 
GBP 16.5 billion (USD 21.8 billion) over four years, with 
a major emphasis on AI technologies that promise to 
revolutionize warfare.
Others
Sweden: National Approach to Artificial Intelligence
Taiwan: Taiwan AI Action Plan
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Published Strategies
2019
Estonia
• 
AI Strategy: National AI Strategy 2019–2021
• 
Responsible Organization: Ministry of Economic Affairs 
and Communications (MKM)
• 
Highlights: The strategy emphasizes actions necessary 
for both the public and private sectors to take to increase 
investment in AI research and development, while also 
improving the legal environment for AI in Estonia. In 
addition, it hammers out the framework for a steering 
committee that will oversee the implementation and 
monitoring of the strategy.
• 
Funding (December 2020 conversion rate): EUR 10 
million (USD 12 million) up to 2021
• 
Recent Updates: The Estonian government released an 
update on the AI taskforce in May 2019.
Russia
• 
AI Strategy: National Strategy for the Development of 
Artificial Intelligence
• 
Responsible Organizations: Ministry of Digital 
Development, Communications and Mass Media; 
Government of the Russian Federation
• 
Highlights: The Russian AI strategy places a strong 
emphasis on its national interests and lays down 
guidelines for the development of an “information 
society” between 2017 and 2030. These include a 
national technology initiative, departmental projects 
for federal executive bodies, and programs such as the 
Digital Economy of the Russian Federation, designed to 
implement the AI framework across sectors. 
• 
Funding: N/A
• 
Recent Updates: In December 2020, Russian president 
Vladmir Putin took part in the Artificial Intelligence 
Journey Conference, where he presented four ideas for AI 
policies: establishing experimental legal frameworks for 
the use of AI, developing practical measures to introduce 
AI algorithms, providing neural network developers with 
competitive access to big data, and boosting private 
investment in domestic AI industries.
Singapore
• 
AI Strategy: National Artificial Intelligence Strategy
• 
Responsible Organization: Smart Nation and Digital 
Government Office (SNDGO)
• 
Highlights: Launched by Smart Nation Singapore, a 
government agency that seeks to transform Singapore’s 
economy and usher in a new digital age, the strategy 
identifies five national AI projects in the following fields: 
transport and logistics, smart cities and estates, health 
care, education, and safety and security.
• 
Funding (December 2020 conversion rate): While the 
2019 strategy does not mention funding, in 2017 the 
government launched its national program, AI Singapore, 
with a pledge to invest SGD 150 million (USD 113 million) 
over five years. 
• 
Recent Updates: In November 2020, SNDGO published 
its inaugural annual update on the Singaporean 
government’s data protection efforts. It describes the 
measures taken to date to strengthen public sector data 
security and to safeguard citizens’ private data.
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2019 (continued)
United States
• 
AI Strategy: American AI Initiative
• 
Responsible Organization: The White House
• 
Highlights: The American AI Initiative prioritizes 
the need for the federal government to invest in AI 
R&D, reduce barriers to federal resources, and ensure 
technical standards for the safe development, testing, 
and deployment of AI technologies. The White House 
also emphasizes developing an AI-ready workforce and 
signals a commitment to collaborating with foreign 
partners while promoting U.S. leadership in AI. The 
initiative, however, lacks specifics on the program’s 
timeline, whether additional research will be dedicated 
to AI development, and other practical considerations.
• 
Funding: N/A 
• 
Recent Updates: The U.S. government released its 
year one annual report in February 2020, followed in 
November by the first guidance memorandum for federal 
agencies on regulating artificial intelligence applications 
in the private sector, including principles that encourage 
AI innovation and growth and increase public trust and 
confidence in AI technologies. The National Defense 
Authorization Act (NDAA) for Fiscal Year 2021 called for a 
National AI Initiative to coordinate AI research and policy 
across the federal government.
South Korea
• 
AI Strategy: National Strategy for Artificial Intelligence
• 
Responsible Organization: Ministry of Science, ICT and 
Future Planning (MSIP) 
• 
Highlights: The Korean strategy calls for plans to 
facilitate the use of AI by businesses and to streamline 
regulations to create a more favorable environment for 
the development and use of AI and other new industries. 
The Korean government also plans to leverage its 
dominance in the global supply of memory chips to build 
the next generation of smart chips by 2030.
• 
Funding (December 2020 conversion rate):  
KRW 2.2 trillion (USD 2 billion)
• 
Recent Updates: N/A
Others
Colombia: National Policy for Digital Transformation 
and Artificial Intelligence
Czech Republic: National Artificial Intelligence 
Strategy of the Czech Republic
Lithuania: Lithuanian Artificial Intelligence Strategy: A 
Vision for the Future
Luxembourg: Artificial Intelligence: A Strategic Vision 
for Luxembourg
Malta: Malta: The Ultimate AI Launchpad
Netherlands: Strategic Action Plan for Artificial 
Intelligence
Portugal: AI Portugal 2030
Qatar: National Artificial Intelligence for Qatar
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Published Strategies
2020
Indonesia
• 
AI Strategy: National Strategy for the Development of 
Artificial Intelligence (Stranas KA)
• 
Responsible Organizations: Ministry of Research 
and Technology (Menristek), National Research and 
Innovation Agency (BRIN), Agency for the Assessment and 
Application of Technology (BPPT)
• 
Strategy Highlights: The Indonesian strategy aims 
to guide the country in developing AI between 2020 
and 2045. It focuses on education and research, health 
services, food security, mobility, smart cities, and public 
sector reform.
• 
Funding: N/A
• 
Recent Updates: None
Saudi Arabia
• 
AI Strategy: National Strategy on Data and AI (NSDAI)
• 
Responsible Organization: Saudi Data and Artificial 
Intelligence Authority (SDAIA)
• 
Highlights: As part of an effort to diversify the country’s 
economy away from oil and boost the private sector, the 
NSDAI aims to accelerate AI development in five critical 
sectors: health care, mobility, education, government, 
and energy. By 2030, Saudi Arabia intends to train 20,000 
data and AI specialists, attract USD 20 billion in foreign 
and local investment, and create an environment that 
will attract at least 300 AI and data startups. 
• 
Funding: N/A
• 
Recent Updates: During the summit where the 
Saudi government released its strategy, the country’s 
National Center for Artificial Intelligence (NCAI) signed 
collaboration agreements with China’s Huawei and 
Alibaba Cloud to design AI-related Arabic-language 
systems.
Others
Hungary: Hungary’s Artificial Intelligence Strategy
Norway: National Strategy for Artificial Intelligence
Serbia: Strategy for the Development of Artificial 
Intelligence in the Republic of Serbia for the Period 
2020–2025
Spain: National Artificial Intelligence Strategy
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Strategies in Development
 (AS OF DECEMBER 2020)
Strategies in Public Consultation
Brazil
• 
AI Strategy Draft: Brazilian Artificial Intelligence Strategy
• 
Responsible Organization: Ministry of Science, 
Technology and Innovation (MCTI)
• 
Highlights: Brazil’s national AI strategy was announced 
in 2019 and is currently in the public consultation stage. 
According to the OECD, the strategy aims to cover 
relevant topics bearing on AI, including its impact on the 
economy, ethics, development, education, and jobs, and 
to coordinate specific public policies addressing such 
issues.
• 
Funding: N/A
• 
Recent Updates: In October 2020, the country’s largest 
research facility dedicated to AI was launched in 
collaboration with IBM, the University of São Paulo, and 
the São Paulo Research Foundation.
Italy
• 
AI Strategy Draft: Proposal for an Italian Strategy for 
Artificial Intelligence
• 
Responsible Organization: Ministry of Economic 
Development (MISE) 
• 
Highlights: This document provides the proposed 
strategy for the sustainable development of AI, aimed 
at improving Italy’s competitiveness in AI. It focuses on 
improving AI-based skills and competencies, fostering AI 
research, establishing a regulatory and ethical framework 
to ensure a sustainable ecosystem for AI, and developing 
a robust data infrastructure to fuel these developments.
• 
Funding (December 2020 conversion rate): EUR 1 
billion (USD 1.1 billion) through 2025 and expected 
matching funds from the private sector, bringing the total 
investment to EUR 2 billion.
• 
Recent Updates: None
Others
Cyprus: National Strategy for Artificial Intelligence
Ireland: National Irish Strategy on Artificial Intelligence
Poland: Artificial Intelligence Development Policy in 
Poland
Uruguay: Artificial Intelligence Strategy for Digital 
Government
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Strategies Announced
Argentina
• 
Related Document: N/A
• 
Responsible Organization: Ministry of Science, 
Technology and Productive Innovation (MINCYT)
• 
Status: Argentina’s AI plan is a part of the Argentine 
Digital Agenda 2030 but has not yet been published. It is 
intended to cover the decade between 2020 and 2030, 
and reports indicate that it has the potential to reap huge 
benefits for the agricultural sector.
Australia
• 
Related Documents: Artificial Intelligence Roadmap /  
An AI Action Plan for all Australians
• 
Responsible Organizations: Commonwealth Scientific 
and Industrial Research Organisation (CSIRO), Data 61, 
and the Australian government
• 
Status: The Australian government published a road 
map in 2019 (in collaboration with the national science 
agency, CSIRO) and a discussion paper of an AI action 
plan in 2020 as frameworks to develop a national 
AI strategy. In its 2018–19 budget, the Australian 
government earmarked AUD 29.9 million (USD 22.2 
million [December 2020 conversation rate]) over four 
years to strengthen the country’s capabilities in AI and 
machine learning (ML). In addition, CSIRO published a 
research paper on Australia’s AI Ethics Framework in 2019 
and launched a public consultation, which is expected to 
produce a forthcoming strategy document.
Turkey
• 
Related Document: N/A
• 
Responsible Organizations: Presidency of the Republic 
of Turkey Digital Transformation Office; Ministry of 
Industry and Technology; Scientific and Technological 
Research Council of Turkey; Science, Technology and 
Innovation Policies Council
• 
Status: The strategy has been announced but not yet 
published. According to media sources, it will focus 
on talent development, scientific research, ethics and 
inclusion, and digital infrastructure.
Others
Austria: Artificial Intelligence Mission Austria  
(official report)
Bulgaria: Concept for the Development of Artificial 
Intelligence in Bulgaria Until 2030 (concept document)
Chile: National AI Policy (official announcement)
Israel: National AI Plan (news article)
Kenya: Blockchain and Artificial Intelligence Taskforce 
(news article) 
Latvia: On the Development of Artificial Intelligence 
Solutions (official report)
Malaysia: National Artificial Intelligence (Al) Framework 
(news article)
New Zealand: Artificial Intelligence: Shaping a Future 
New Zealand (official report)
Sri Lanka: Framework for Artificial Intelligence (news 
article)
Switzerland: Artificial Intelligence (official guidelines)
Tunisia: National Artificial Intelligence Strategy (task 
force announced)
Ukraine: Concept of Artificial Intelligence Development 
in Ukraine AI (concept document)
Vietnam: Artificial Intelligence Development Strategy 
(official announcement)
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
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Read more on AI national strategies:
• 
Tim Dutton: An Overview of National AI Strategies
• 
Organisation for Economic Co-operation and Development: OECD AI Policy Observatory
• 
Canadian Institute for Advanced Research: Building an AI World, Second Edition
• 
Inter-American Development Bank: Artificial Intelligence for Social Good in Latin America and the Caribbean:  
The Regional Landscape and 12 Country Snapshots
7.1 NATIONAL 
AND REGIONAL 
AI STRATEGIES
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
National AI Strategies and Human Rights
Table 7.1.1: Mapping human rights 
referenced in national AI strategies
HUMAN RIGHTS 
MENTIONED
STATES/REGIONAL 
ORGANIZATIONS
The right to privacy
Australia, Belgium, China, 
Czech Republic, Germany, 
India, Italy, Luxembourg, Malta, 
Netherlands, Norway, Portugal, 
Qatar, South Korea, United 
States
The right 
to equality/
nondiscrimination
Australia, Belgium, Czech 
Republic, Denmark, Estonia, EU, 
France, Germany, Italy, Malta, 
Netherlands, Norway
The right to an 
effective remedy
Australia (responsibility 
and ability to hold humans 
responsible), Denmark, Malta, 
Netherlands
The rights to 
freedom of thought, 
expression, 
and access to 
information
France, Netherlands,  
Russia
The right to work
France, Russia
In 2020, Global Partners Digital and Stanford’s 
Global Digital Policy Incubator published a 
report examining governments’ national AI 
strategies from a human rights perspective, 
titled “National Artificial Intelligence Strategies 
and Human Rights: A Review.” The report 
assesses the extent to which governments 
and regional organizations have incorporated 
human rights considerations into their national 
AI strategies and made recommendations to 
policymakers looking to develop or review AI 
strategies in the future. 
The report found that among the 30 states and 
two regional strategies (from the European 
Union and the Nordic-Baltic states), a number 
of strategies refer to the impact of AI on human 
rights, with the right to privacy as the most 
commonly mentioned, followed by equality 
and nondiscrimination (Table 6.1.1). However, 
very few strategy documents provide deep 
analysis or concrete assessment of the impact 
of AI applications on human rights. Specifics 
as to how and the depth to which human 
rights should be protected in the context of 
AI is largely missing, in contrast to the level of 
specificity on other issues such as economic 
competitiveness and innovation advantage. 


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Given the scale of the opportunities and the challenges 
presented by AI, a number of international efforts have 
recently been announced that aim to develop multilateral 
AI strategies. This section provides an overview of those 
international initiatives from governments committed to 
working together to support the development of AI for all. 
These multilateral initiatives on AI suggest that 
organizations are taking a variety of approaches to 
tackle the practical applications of AI and scale those 
solutions for maximum global impact. Many countries 
turn to international organizations for global AI norm 
formulation, while others engage in partnerships or 
bilateral agreements. Among the topics under discussion, 
the ethics of AI—or the ethical challenges raised by current 
and future applications of AI—stands out as a particular 
focus area for intergovernmental efforts. 
Countries such as Japan, South Korea, the United 
Kingdom, the United States, and members of the European 
Union are active participants of intergovernmental 
efforts on AI. A major AI powerhouse, China, on the other 
hand, has opted to engage in a number of science and 
technology bilateral agreements that stress cooperation 
on AI as part of the Digital Silk Road under the Belt 
and Road (BRI) initiative framework. For example, AI is 
mentioned in China’s economic cooperation under the BRI 
Initiative with the United Arab Emirates. 
INTERGOVERNMENTAL 
INITIATIVES 
Intergovernmental working groups consist of experts and 
policymakers from member states who study and report 
on the most urgent challenges related to developing and 
deploying AI and then make recommendations based on 
their findings. These groups are instrumental in identifying 
and developing strategies for the most pressing issues in AI 
technologies and their applications. 
Working Groups
Global Partnership on AI (GPAI)
• 
Participants: Australia, Brazil, Canada, France, Germany, 
India, Italy, Japan, Mexico, the Netherlands, New 
Zealand, South Korea, Poland, Singapore, Slovenia, 
Spain, the United Kingdom, the United States, and the 
European Union (as of December 2020)
• 
Host of Secretariat: OECD
• 
Focus Areas: Responsible AI; data governance; the future 
of work; innovation and commercialization
• 
Recent Activities: Two International Centres of 
Expertise—the International Centre of Expertise in 
Montreal for the Advancement of Artificial Intelligence 
and the French National Institute for Research in Digital 
Science and Technology (INRIA) in Paris—are supporting 
the work in the four focus areas and held the Montreal 
Summit 2020 in December 2020. Moreover, the data 
governance working group published the beta version of 
the group’s framework in November 2020.
OECD Network of Experts on AI (ONE AI)
• 
Participants: OECD countries
• 
Host: OECD
• 
Focus Areas: Classification of AI; implementing 
trustworthy AI; policies for AI; AI compute
• 
Recent Activities: ONE AI convened its first meeting in 
February 2020, when it also launched the OECD AI Policy 
Observatory. In November 2020, the working group on 
the classification of AI presented the first look at an AI 
classification framework based on OECD’s definition of AI 
divided into four dimensions (context, data and input, AI 
model, task and output) that aims to guide policymakers 
in designing adequate policies for each type of AI system.
High-Level Expert Group on Artificial Intelligence (HLEG) 
• 
Participants: EU countries
• 
Host: European Commission
• 
Focus Areas: Ethics guidelines for trustworthy AI
• 
Recent Activities: Since its launch at the recommendation 
7.2 INTERNATIONAL 
COLLABORATION ON AI
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AI POLICY AND  
NATIONAL STRATEGIES
7.2 INTERNATIONAL 
COLLABORATION 
ON AI


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of the EU AI strategy in 2018, HLEG presented the EU Ethics 
Guidelines for Trustworthy Artificial Intelligence and a 
series of policy and investment recommendations, as 
well as an assessment checklist related to the guidelines. 
Ad Hoc Expert Group (AHEG) for the Recommendation 
on the Ethics of Artificial Intelligence
• 
Participants: United Nations Educational, Scientific and 
Cultural Organization (UNESCO) member states
• 
Host: UNESCO
• 
Focus Areas: Ethical issues raised by the development 
and use of AI 
• 
Recent Activities: The AHEG produced a revised first draft 
Recommendation on the Ethics of Artificial Intelligence, 
which was transmitted in September 2020 to Member States 
of UNESCO for their comments by December 31, 2020.
Summits and Meetings
AI for Good Global Summit
• 
Participants: Global (with the United Nations and its 
agencies)
• 
Hosts: International Telecommunication Union, XPRIZE 
Foundation
• 
Focus Areas: Trusted, safe, and inclusive development of 
AI technologies and equitable access to their benefits
AI Partnership for Defense
• 
Participants: Australia, Canada, Denmark, Estonia, 
Finland, France, Israel, Japan, Norway, South Korea, 
Sweden, the United Kingdom, and the United States
• 
Hosts: Joint Artificial Intelligence Center, U.S. 
Department of Defense
• 
Focus Areas: AI ethical principles for defense
China-Association of Southeast Asian Nations (ASEAN) 
AI Summit
• 
Participants: Brunei, Cambodia, China, Indonesia, Laos, 
Malaysia, Myanmar, the Philippines, Singapore, Thailand, 
and Vietnam
• 
Hosts: China Association for Science and Technology, 
Guangxi Zhuang Autonomous Region, China
• 
Focus Areas: Infrastructure construction, digital 
economy, and innovation-driven development
BILATERAL AGREEMENTS
Bilateral agreements focusing on AI are another form 
of international collaboration that has been gaining in 
popularity in recent years. AI is usually included in the 
broader context of collaborating on the development of 
digital economies, though India stands apart for investing 
in developing multiple bilateral agreements specifically 
geared toward AI.
India and United Arab Emirates
Invest India and the UAE Ministry of Artificial Intelligence 
signed a memorandum of understanding in July 2018 
to collaborate on fostering innovative AI ecosystems 
and other policy concerns related to AI. Two countries 
will convene a working committee aimed at increasing 
investment in AI startups and research activities in 
partnership with the private sector.
India and Germany
It was reported in October 2019 that India and Germany 
likely will sign an agreement including partnerships on the 
use of artificial intelligence (especially in farming).
United States and United Kingdom
The U.S. and the U.K. announced a declaration in 
September 2020, through the Special Relationship 
Economic Working Group, that the two countries will 
enter into a bilateral dialogue on advancing AI in line with 
shared democratic values and further cooperation in AI 
R&D efforts.
India and Japan
India and Japan were said to have finalized an agreement 
in October 2020 that focuses on collaborating on digital 
technologies, including 5G and AI. 
French and Germany
France and Germany signed a road map for a Franco-
German Research and Innovation Network on artificial 
intelligence as part of the Declaration of Toulouse 
in October 2019 to advance European efforts in the 
development and application of AI, taking into account 
ethical guidelines.
CHAPTER 7:
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NATIONAL STRATEGIES
7.2 INTERNATIONAL 
COLLABORATION 
ON AI


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2020 (Request)
2020 (Enacted)
2021 (Request)
0
500
1,000
1,500
Budget (in Millions of U.S. Dollars)
U.S. FEDERAL BUDGET for NON-DEFENSE AI R&D, FY 2020-21
Source: U.S. NITRD Program, 2020 | Chart: 2021 AI Index Report
FEDERAL BUDGET FOR  
NON-DEFENSE AI R&D 
In September 2019, the White House 
National Science and Technology Council 
released a report attempting to total 
up all public-sector AI R&D funding, the 
first time such a figure was published. 
This funding is to be disbursed as grants 
for government laboratories or research 
universities or in the form of government 
contracts. These federal budget figures, 
however, do not include substantial AI 
R&D investments by the Department of 
Defense (DOD) and the intelligence sector, 
as they were withheld from publication for 
national security reasons. 
As shown in Figure 7.3.1, federal civilian 
agencies—those agencies that are not part 
of the DOD or the intelligence sector—
allocated USD 973.5 million to AI R&D 
for FY 2020, a figure that rose to USD 1.1 
billion once congressional appropriations 
and transfers were factored in. For FY 
2021, federal civilian agencies budgeted 
USD 1.5 billion, which is almost 55% 
higher than its 2020 request. 
7.3 U.S. PUBLIC INVESTMENT IN AI
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
This section examines public investment in AI in the United States based on data from the U.S. Networking and Information 
Technology Research and Development (NITRD) program and Bloomberg Government. 
Figure 7.3.1
Federal civilian agencies—those 
agencies that are not part of the 
DOD or the intelligence sector—
allocated USD 973.5 million to 
AI R&D for FY 2020, a figure 
that rose to USD 1.1 billion once 
congressional appropriations 
and transfers were factored in.


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2018 (Enacted)
2019 (Enacted)
2020 (Enacted)
2021 (Request)
0
1,000
2,000
3,000
4,000
5,000
Budget (in Millions of U.S. Dollars)
927
841
DOD Reported
Budget on AI R&D
DOD Reported
Budget on AI R&D
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH DEVELOPMENT, TEST, and EVALUATION (RDT&E), FY 2018-20
Sources: Bloomberg Government & U.S. Department of Defense, 2020 | Chart: 2021 AI Index Report
Figure 7.3.2
U.S. DEPARTMENT OF DEFENSE AI 
R&D BUDGET REQUEST 
While the official DOD budget is not publicly available, 
Bloomberg Government has analyzed the department’s 
publicly available budget request for research, 
development, test, and evaluation (RDT&E)— data that 
sheds light on its spending on AI R&D. 
With 305 unclassified DOD R&D programs specifying the use 
of AI or ML technologies, the combined U.S. military budget 
for AI R&D in FY 2021 is USD 5.0 billion (Figure 7.3.2). This 
figure appears consistent with the USD 5.0 billion enacted 
the previous year. However, the FY 2021 figure reflects 
a budget request, rather than a final enacted budget. 
As noted above, once congressional appropriations are 
factored in, the true level of funding available to DOD AI R&D 
programs in FY 2021 may rise substantially.
The top five projects set to receive the highest amount of 
AI R&D investment in FY 2021: 
• 
Rapid Capability Development and Maturation, by the 
U.S. Army (USD 284.2 million)
• 
Counter WMD Technologies and Capabilities 
Development, by the DOD Threat Reduction Agency 
(USD 265.2 million) 
• 
Algorithmic Warfare Cross-Functional Team (Project 
Maven), by the Office of the Secretary of Defense (USD 
250.1 million) 
• 
Joint Artificial Intelligence Center (JAIC), by the Defense 
Information Systems Agency (USD 132.1 million)
• 
High Performance Computing Modernization Program, 
by the U.S. Army (USD 99.6 million)
In addition, the Defense Advanced Research Projects 
Agency (DARPA) alone is investing USD 568.4 million in AI 
R&D, an increase of USD 82 million from FY 2020.
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
Important data caveat: This chart illustrates the challenge of working with contemporary government data sources 
to understand spending on AI. By one measure—the requests that include AI-relevant keywords—the DOD is requesting 
more than USD 5 billion for AI-specific research development in 2021 . However, DOD’s own accounting produces a 
radically smaller number: USD 841 million. This relates to the issue of defining where an AI system ends and another 
system begins; for instance, an initiative that uses AI for drones may also count hardware-related expenditures for the 
drones within its “AI” budget request, though the AI software component will be much smaller.


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USD 1.5 billion agencies spent in FY 2019 (Figure 7.3.3). 
AI spending in 2020 was more than six times higher than 
what it was just five years ago—about USD 300 million in 
FY 2015. However, to put this in perspective, the federal 
government spent USD 682 billion on contracts in FY 2020, 
so AI currently represents 0.25% of government spending.
Contract Spending by Department and Agency
Figure 7.3.4 shows that in FY 2020, the DOD spent more on 
AI-related contracts than any other federal department 
or agency (USD 1.4 billion). In second and third place 
are NASA (USD 139.1 million) and the Department of 
Homeland Security (USD 112.3 million). DOD, NASA, and 
the Department of Health and Human Services top the 
list for the most contract spending on AI over the past 10 
years combined (Figure 7.3.5). In fact, DOD’s total contract 
spending on AI from 2001 to 2020 (USD 3.9 billion) is more 
than what was spent by the other 44 departments and 
agencies combined (USD 2.9 billion) over the same period.
Looking ahead, DOD spending on AI contracts is only 
expected to grow as the Pentagon’s Joint Artificial 
Intelligence Center (JAIC), established in June 2018, is 
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
500
1,000
1,500
2,000
Contract Spending (in Millions of U.S. Dollars)
1,837
U.S. GOVERNMENT TOTAL CONTRACT SPENDING on AI, FY 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Figure 7.3.3
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
U.S. GOVERNMENT AI-RELATED 
CONTRACT SPENDING
Another indicator of public investment in AI technologies is 
the level of spending on government contracts across the 
federal government. Contracting for products and services 
supplied by private businesses typically occupies the largest 
share of an agency’s budget. Bloomberg Government built 
a model that captures contract spending on AI technologies 
by adding up all contracting transactions that contain a 
set of more than 100 AI-specific keywords in their titles or 
descriptions. The data reveals that the amount the federal 
government spends on contracts for AI products and 
services has reached an all-time high and shows no sign of 
slowing down. However, note that during the procurement 
process, vendors may add a bunch of keywords into their 
applications, so some of these things may have a relatively 
small AI component relative to other parts of technology.
Total Contract Spending
Federal departments and agencies spent a combined 
USD 1.8 billion on unclassified AI-related contracts in FY 
2020. This represents a more than 25% increase from the 


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0
500
1000
1500
2000
2500
3000
3500
4000
Contract Spending (in Millions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and
Space Administration (NASA)
Department of Health and
Human Services (HHS)
Department of the Treasury
(TREAS)
Department of Homeland
Security (DHS)
Department of Veterans A airs
(VA)
Department of Commerce
(DOC)
Department of Agriculture
(USDA)
General Services
Administration (GSA)
Department of State (DOS)
TOP 10 CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2001-20 (SUM)
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
0
200
400
600
800
1,000
1,200
1,400
Contract Spending (in Millions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and Space
Administration (NASA)
Department of Homeland Security
(DHS)
Department of Health and Human
Services (HHS)
Department of Commerce (DOC)
Department of the Treasury
(TREAS)
Department of Veterans A airs
(VA)
Securities and Exchange
Commission (SEC)
Department of Agriculture (USDA)
Department of Justice (DOJ)
TOP 10 CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2020
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Figure 7.3.4
Figure 7.3.5
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.3 U.S. PUBLIC 
INVESTMENT 
IN AI
still in the early stages of driving DOD’s AI spending. In 
2020, JAIC awarded two massive contracts, one to Booz 
Allen Hamilton for the five-year, USD 800 million Joint 
Warfighter program, and another to Deloitte Consulting for 
a four-year, USD 106 million enterprise cloud environment 
for the JAIC, known as the Joint Common Foundation.


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107Th
(2001-2002)
108Th
(2003-2004)
109Th
(2005-2006)
110Th
(2007-2008)
111Th
(2009-2010)
112Th
(2011-2012)
113Th
(2013-2014)
114Th
(2015-2016)
115Th
(2017-2018)
116th
(2019-2020)
0
100
200
300
400
500
Number of Mentions
486
149
22
10
16
15
17
4
8
7
243
173
44
66
39
70
MENTIONS of AI in U.S. CONGRESSIONAL RECORD by LEGISLATIVE SESSION, 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report
Congressional Research Service Reports
Committee Reports
Legislation
As AI gains attention and importance, policies and 
initiatives related to the technology are becoming higher 
priorities for governments, private companies, technical 
organizations, and civil society. This section examines 
how three of these four are setting the agenda for AI 
policymaking, including the legislative and monetary 
authority of national governments, as well as think tanks, 
civil society, and the technology and consultancy industry. 
LEGISLATION RECORDS ON AI
The number of congressional and parliamentary 
records on AI is an indicator of governmental interest 
in developing AI capabilities—and legislating issues 
pertaining to AI. In this section, we use data from 
Bloomberg and McKinsey & Company to ascertain the 
7.4 AI AND POLICYMAKING
CHAPTER 7:
AI POLICY AND  
NATIONAL STRATEGIES
7.4 AI AND 
POLICYMAKING
Figure 7.4.1
number of these records and how that number has 
evolved in the last 10 years. 
Bloomberg Government identified all legislation (passed 
or introduced), reports published by congressional 
committees, and CRS reports that referenced one or more 
AI-specific keywords. McKinsey & Company searched for 
the terms “artificial intelligence” and “machine learning” 
on the websites of the U.S. Congressional Record, the U.K. 
Parliament, and the Parliament of Canada. For the United 
States, each count indicates that AI or ML was mentioned 
during a particular event contained in the Congressional 
Record, including the reading of a bill; for the U.K. and 
Canada, each count indicates that AI or ML was mentioned 
in a particular comment or remark during the proceedings.1
1 If a speaker or member mentioned artificial intelligence (AI) or machine learning (ML) multiple times within remarks, or multiple speakers mentioned AI or ML within the same event, it appears only 
once as a result. Counts for AI and ML are separate, as they were conducted in separate searches. Mentions of the abbreviations “AI” or “ML” are not included.


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2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
20
40
60
80
100
120
140
Number of Mentions
120
129
92
27
0
9
8
1
1
1
101
92
28
28
25
23
67
6
7
MENTIONS of AI and ML in the PROCEEDINGS of U.S. CONGRESS, 2011-20
Sources: U.S. Congressional Record website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
U.S. Congressional Record
The 116th Congress (January 1, 2019–January 3, 2021) is 
the most AI-focused congressional session in history. The 
number of mentions of AI by this Congress in legislation, 
committee reports, and CRS reports is more than triple 
that of the 115th Congress. Congressional interest in AI 
has continued to accelerate in 2020. Figure 7.4.1 shows 
that during this congressional session, 173 distinct 
pieces of legislation either focused on or contained 
language about AI technologies, their development, 
use, and rules governing them. During that two-year 
period, various House and Senate committees and 
CHAPTER 7:
AI POLICY AND  
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7.4 AI AND 
POLICYMAKING
Figure 7.4.2
subcommittees commissioned 70 reports on AI, while 
the CRS, tasked as a fact-finding body for members of 
Congress, published 243 about AI or referencing AI. 
Mentions of AI and ML in Congressional/
Parliamentary Proceedings
As shown in Figures 7.4.2–7.4.5, the number of mentions 
of artificial intelligence and machine learning in the 
proceedings of the U.S. Congress and the U.K. parliament 
continued to rise in 2020, while there were fewer 
mentions in the parliamentary proceedings of Canada. 


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2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
50
100
150
200
250
300
Number of Mentions
283
192
183
138
51
0
4
5
7
1
246
158
138
179
34
42
37
MENTIONS of AI and ML in the PROCEEDINGS of U.K. PARLIAMENT, 2011-20
Sources: Parliament of U.K. website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
10
20
30
40
Number of Mentions
34
38
18
21
0
0
0
0
0
2
35
33
21
17
3
MENTIONS of AI and ML in the PROCEEDINGS of CANADIAN PARLIAMENT, 2011-20
Sources: Canadian Parliament website, the McKinsey Global Institute, 2020 | Chart: 2021 AI Index Report
Machine Learning
Artificial Intelligence
CHAPTER 7:
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7.4 AI AND 
POLICYMAKING
Figure 7.4.3
Figure 7.4.4


24
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2 See Science & Technology Review and Scientific American for more details.
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
200
400
600
800
1,000
Number of Mentions
225
MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD, 2011-20
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report
Figure 7.4.5
CENTRAL BANKS
Central banks play a key role in conducting currency and 
monetary policy in a country or a monetary union. As 
with many other institutions, central banks are tasked 
with integrating AI into their operations and relying on 
big data analytics to assist them with forecasting, risk 
management, and financial supervision. 
Prattle, a leading provider of automated investment 
research solutions, monitors mentions of AI in the 
communications of central banks, including meeting 
minutes, monetary policy papers, press releases, 
speeches, and other official publications. 
Figure 7.4.5 shows a significant increase in the mention 
of AI across 16 central banks over the past 10 years, 
with the number reaching a peak of 1,020 in 2019. The 
sharp decline in 2020 can be explained by the COVID-19 
pandemic as most central bank communications focused 
on responses to the economic downturn. Moreover, 
the Federal Reserve in the United States, Norges Bank 
in Norway, and the European Central Bank top the 
list for the most aggregated number of AI mentions in 
communications in the past five years (Figure 7.4.6). 


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0
200
400
600
800
1,000
1,200
1,400
1,600
1,800 2,000
Number of Mentions
Federal Reserve
Norges Bank
European Central Bank
Reserve Bank of India
Bank of England
Bank of Israel
Bank of Japan
Bank of Korea
Reserve Bank of Australia
Reserve bank of New Zealand
Bank of Taiwan
Bank of Canada
Sveriges Riksbank
Swedish Riksbank
Central Bank of the Republic of Turkey
Central Bank of Brazil
MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD by BANK, 2016-20 (SUM)
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report
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7.4 AI AND 
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Figure 7.4.6


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0
20
40
60
80
100
120
140
160
Number of Policy Products
Innovation & Technology
Int'l Affairs & Int'l Security
Industry & Regulation
Workforce & Labor
Government & Public Administration
Privacy, Safety & Security
Ethics
Justice & Law Enforcement
Equity & Inclusion
Education & Skills
Social & Behavioral Sciences
Health & Biological Sciences
Communications & Media
Democracy
Humanities
Energy & Environment
Physical Sciences
U.S. AI POLICY PRODUCTS by TOPIC, 2019-20 (SUM)
Source: Stanford HAI & AI Index, 2020 | Chart: 2021 AI Index Report
Secondary Topic
Primary Topic
U.S. AI POLICY PAPERS
What are the AI policy initiatives outside national and 
intergovernmental governments? We monitored 42 
prominent organizations that deliver policy papers on 
topics related to AI and assessed the primary topic as 
well as the secondary topic on policy papers published 
in 2019 and 2020. (See the Appendix for a complete list 
of organizations included.) Those organizations are 
either U.S.-based or have a sizable presence in the United 
States, and we grouped them into three categories: think 
tanks, policy institutes and academia (27); civil society 
organizations, associations and consortiums (9); and 
industry and consultancy (6). 
AI policy papers are defined as research papers, research 
reports, blog posts, and briefs that focus on a specific policy 
issue related to AI and provide clear recommendations 
CHAPTER 7:
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7.4 AI AND 
POLICYMAKING
Figure 7.4.7
for policymakers. Primary topics mean that such a topic is 
the main focus of the policy paper, while secondary topics 
mean that the policy paper either briefly touches on the 
topic or the topic is a sub-focus of the paper. 
Combined data for 2019 and 2020 suggests that the topics 
of innovation and technology, international affairs and 
international security, and industry and regulation are 
the main focuses of AI policy papers in the United States 
(Figure 7.4.7). Fewer documents placed a primary focus 
on topics related to AI ethics—such as ethics, equity and 
inclusion; privacy, safety and security; and justice and law 
enforcement—which have largely been secondary topics. 
Moreover, topics bearing on the physical sciences, energy 
and environment, humanities, and democracy have 
received the least attention in U.S. AI policy papers. 


27
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APPENDIX
CHAPTER 7:  
AI POLICY AND  
NATIONAL STRATEGIES
APPENDIX
BLOOMBERG GOVERNMENT 
Bloomberg Government (BGOV) is a subscription-
based market intelligence service designed to make 
U.S. government budget and contracting data more 
accessible to business development and government 
affairs professionals. BGOV’s proprietary tools ingest 
and organize semi-structured government data sets 
and documents, enabling users to track and forecast 
investment in key markets. 
Methodology
The BGOV data included in this section was drawn from 
three original sources:
Contract Spending: BGOV’s Contracts Intelligence Tool 
ingests on a twice-daily basis all contract spending data 
published to the beta.SAM.gov Data Bank, and structures 
the data to ensure a consistent picture of government 
spending over time. For the section “U.S. Government 
Contract Spending,” BGOV analysts used FPDS-NG data, 
organized by the Contracts Intelligence Tool, to build a 
model of government spending on artificial intelligence-
related contracts in the fiscal years 2000 through 2021. 
BGOV’s model used a combination of government-
defined produce service codes and more than 100 
AI-related keywords and acronyms to identify AI-related 
contract spending.
Defense RDT&E Budget: BGOV organized all 7,057 
budget line items included in the RDT&E budget request 
based on data available on the DOD Comptroller website. 
For the section “U.S. Department of Defense (DOD) 
Budget,” BGOV used a set of more than a dozen AI-
specific keywords to identify 305 unique budget activities 
related to artificial intelligence and machine learning 
worth a combined USD 5.0 billion in FY 2021.
Congressional Record (available on Congressional 
Record website): BGOV maintains a repository of 
congressional documents, including bills, amendments, 
bill summaries, Congressional Budget Office 
assessments, reports published by congressional 
committees, Congressional Research Service (CRS), and 
others. For the section “U.S. Congressional Record,” 
BGOV analysts identified all legislation (passed or 
introduced), congressional committee reports, and 
CRS reports that referenced one or more of a dozen AI-
specific keywords. Results are organized by a two-year 
congressional session.
LIQUIDNET
Prepared by Jeffrey Banner and Steven Nichols
Source
Liquidnet provides sentiment data that predicts 
the market impact of central bank and corporate 
communications. Learn more about Liquidnet here. 
Examples of Central Bank Mentions
Here are some examples of how AI is mentioned by 
central banks: In the first case, China uses a geopolitical 
environment simulation and prediction platform 
that works by crunching huge amounts of data and 
then providing foreign policy suggestions to Chinese 
diplomats or the Bank of Japan use of AI prediction 
models for foreign exchange rates. For the second 
case, many central banks are leading communications 
through either official documents—for example, on 
July 25, 2019, the Dutch Central Bank (DNB) published 
Guidelines for the use of AI in financial services and 
launched its six “SAFEST” principles for regulated firms 
to use AI responsibly—or a speech on June 4, 2019, by 
the Bank of England’s Executive Director of U.K. Deposit 
Takers Supervision James Proudman, titled “Managing 
Machines: The Governance of Artificial Intelligence,” 
focused on the increasingly important strategic issue of 
how boards of regulated financial services should use AI. 


28
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APPENDIX
MCKINSEY GLOBAL INSTITUTE
Source
Data collection and analysis was performed by the 
McKinsey Global Institute (MGI).
Canada (House of Commons)
Data was collected using the Hansard search feature on 
Parliament of Canada website. MGI searched for the terms 
“Artificial Intelligence” and “Machine Learning” (quotes 
included) and downloaded the results as a CSV. The date 
range was set to “all debates.” Data is as of Dec. 31, 2020. 
Data are available online from Aug. 31, 2002.
Each count indicates that Artificial Intelligence or Machine 
Learning was mentioned in a particular comment or remark 
during the proceedings of the House of Commons. This 
means that within an event or conversation, if a member 
mentions AI or ML multiple times within their remarks, it 
will appear only once. However if, during the same event, 
the speaker mentions AI or ML in separate comments (with 
other speakers in between), it will appear multiple times. 
Counts for Artificial Intelligence or Machine Learning are 
separate, as they were conducted in separate searches. 
Mentions of the abbreviations AI or ML are not included. 
United Kingdom (House of Commons, House of 
Lords, Westminster Hall, and Committees)
Data was collected using the Find References feature of the 
Hansard website of the U.K. Parliament. MGI searched for 
the terms “Artificial Intelligence” and “Machine Learning” 
(quotes included) and catalogued the results. Data is as 
of Dec. 31, 2020. Data are available online from January 1, 
1800 onward. Contains Parliamentary information licensed 
under the Open Parliament Licence v3.0.
As in Canada, each count indicates that Artificial 
Intelligence or Machine Learning was mentioned in a 
particular comment or remark during a proceeding. 
Therefore, if a member mentions AI or ML multiple times 
within their remarks, it will appear only once. However 
if, during the same event, the same speaker mentions 
AI or ML in separate comments (with other speakers in 
between), it will appear multiple times. Counts for Artificial 
Intelligence or Machine Learning are separate, as they 
were conducted in separate searches. Mentions of the 
abbreviations AI or ML are not included. 
United States (Senate and House of 
Representatives)
Data was collected using the advanced search feature 
of the U.S. Congressional Record website. MGI searched 
the terms “Artificial Intelligence” and “Machine Learning” 
(quotes included) and downloaded the results as a 
CSV. The “word variant” option was not selected, and 
proceedings included Senate, House of Representatives, 
and Extensions of Remarks, but did not include the Daily 
Digest. Data is as of Dec. 31, 2020, and data is available 
online from the 104th Congress onward (1995).
Each count indicates that Artificial Intelligence or Machine 
Learning was mentioned during a particular event 
contained in the Congressional Record, including the 
reading of a bill. If a speaker mentioned AI or ML multiple 
times within remarks, or multiple speakers mentioned AI or 
ML within the same event, it would appear only once as a 
result. Counts for Artificial Intelligence or Machine Learning 
are separate, as they were conducted in separate searches. 
Mentions of the abbreviations AI or ML are not included.
U.S. AI POLICY PAPER
Source
Data collection and analysis was performed by Stanford 
Institute of Human-Centered Artificial Intelligence and AI Index.
Organizations
To develop a more nuanced understanding of the 
thought leadership that motivates AI policy, we tracked 
policy papers published by 36 organizations across three 
broad categories including: 
Think Tanks, Policy Institutes & Academia: This includes 
organizations where experts (often from academia and 
the political sphere) provide information and advice 
on specific policy problems. We included the following 
27 organizations: AI PULSE at UCLA Law, American 
Enterprise Institute, Aspen Institute, Atlantic Council, 
Berkeley Center for Long-Term Cybersecurity, Brookings 


29
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APPENDIX
Institution, Carnegie Endowment for International Peace, 
Cato Institute, Center for a New American Security, 
Center for Strategic and International Studies, Council 
on Foreign Relations, Georgetown Center for Security 
and Emerging Technology (CSET), Harvard Belfer Center, 
Harvard Berkman Klein Center, Heritage Foundation, 
Hudson Institute, MacroPolo, MIT Internet Policy Research 
Initiative, New America Foundation, NYU AI Now Institute, 
Princeton School of Public and International Affairs, RAND 
Corporation, Rockefeller Foundation, Stanford Institute 
for Human-Centered Artificial Intelligence (HAI), Stimson 
Center, Urban Institute, Wilson Center.
Civil Society, Associations & Consortiums: Not-for profit 
institutions including community-based organizations 
and NGOs advocating for a range of societal issues. We 
included the following nine organizations: Algorithmic 
Justice League, Alliance for Artificial Intelligence in 
Healthcare, Amnesty International, EFF, Future of Privacy 
Forum, Human Rights Watch, IJIS, Institute for Electrical 
and Electronics Engineers, Partnership on AI
Industry & Consultancy: Professional practices providing 
expert advice to clients and large industry players. We 
included six prominent organizations in this space: Accenture, 
Bain & Co., BCG, Deloitte, Google AI, McKinsey & Company
Methodology
Each broad topic area is based on a collection of underlying 
keywords that describes the content of the specific paper. 
We included 17 topics that represented the majority of 
discourse related to AI between 2019-2020. These topic 
areas and the associated keywords are listed below.
• 
Health & Biological Sciences: medicine, healthcare 
systems, drug discovery, care, biomedical research, 
insurance, health behaviors, COVID-19, global health
• 
Physical Sciences: chemistry, physics, astronomy, earth 
science
• 
Energy & Environment: Energy costs, climate change, 
energy markets, pollution, conservation, oil & gas, 
alternative energy
• 
International Affairs & International Security: 
international relations, international trade, developing 
countries, humanitarian assistance, warfare, regional 
security, national security, autonomous weapons
• 
Justice & Law Enforcement: civil justice, criminal justice, 
social justice, police, public safety, courts
• 
Communications & Media: social media, disinformation, 
media markets, deepfakes
• 
Government & Public Administration: federal 
government, state government, local government, public 
sector efficiency, public sector effectiveness, government 
services, government benefits, government programs, 
public works, public transportation
• 
Democracy: elections, rights, freedoms, liberties, 
personal freedoms
• 
Industry & Regulation: economy, antitrust, M&A, 
competition, finance, management, supply chain, 
telecom, economic regulation, technical standards, 
autonomous vehicle industry & regulation
• 
Innovation & Technology: advancements and 
improvements in AI technology, R&D, intellectual 
property, patents, entrepreneurship, innovation 
ecosystems, startups, computer science, engineering
• 
Education & Skills: early childhood, K-12, higher 
education, STEM, schools, classrooms, reskilling
• 
Workforce & Labor: labor supply and demand, talent, 
immigration, migration, personnel economics, future of 
work
• 
Social & Behavioral Sciences: sociology, linguistics, 
anthropology, ethnic studies, demography, geography, 
psychology, cognitive science
• 
Humanities: arts, music, literature, language, 
performance, theater, classics, history, philosophy, 
religion, cultural studies
• 
Equity & Inclusion: biases, discrimination, gender, 
race, socioeconomic inequality, disabilities, vulnerable 
populations
• 
Privacy, Safety & Security: anonymity, GDPR, 
consumer protection, physical safety, human control, 
cybersecurity, encryption, hacking
• 
Ethics: transparency, accountability, human 
values, human rights, sustainability, explainability, 
interpretability, decision-making norms


Artificial Intelligence
Index Report 2022
CHAPTER 5: 
AI Policy and  
Governance


2
Artificial Intelligence
Index Report 2022
Overview	
  3
Chapter Highlights	
  4
5.1 AI AND POLICYMAKING	
  5
Global Legislation Records on AI	
  5
	
By Geographic Area	
  6
	
Federal AI Legislation in the 
	
United States	
  7
	
Highlight: A Closer Look 
	
at the Legislation	
  8
State-Level AI Legislation 
in the United States	
  9
	
By State	
 10
	
Sponsorship by Political Party	
 11
Mentions of AI in Legislative Records	
 12
	
AI Mentions in U.S. Congressional 
	
Records	
 12
	
AI Mentions in Global Legislative 
	
Proceedings	
 13
	
By Geographic Area	
 14
U.S. AI Policy Papers	
 15
	
By Topic	
 16
5.2 U.S. PUBLIC INVESTMENT IN AI	
 17 
Federal Budget for Nondefense AI R&D	
 17
U.S. Department of Defense 
Budget Request	
 18
	
Highlight: DOD Top Five 
	
Highest-Funded Programs	
 19
	
DOD AI R&D Spending by Department	  20
U.S. Government AI-Related  
Contract Spending	
 21
	
Total Contract Spending	
 21
	
Contract Spending by Department 
	
and Agency	
 22
	
Highlight: Largest Contract for Five
	
Top-Spending Departments in 2021	
 24
APPENDIX	
 25 
Chapter Preview
CHAPTER 5:
ACCESS THE PUBLIC DATA
CHAPTER 5: AI POLICY AND GOVERNANCE


3
Chapter 5 Preview
Artificial Intelligence
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Overview
As AI has become an increasingly ubiquitous topic in the last decade, 
intergovernmental, national, and regional organizations have worked to 
develop policies and strategies around AI governance. These actors are 
driven by the understanding that it is imperative to find ways to address 
the ethical and societal concerns surrounding AI, while maximizing 
its benefits. Active and informed governance of AI technologies has 
become a priority for many governments around the world.
This chapter examines the intersection of AI and governance, and takes 
a closer look at how governments in different countries, regions, and 
U.S. states are working to manage AI technologies. It begins by looking 
at AI policymaking across the globe and within the United States, 
exploring which countries and political actors are most keen to advance 
AI legislation, and what kind of AI subtopics, from privacy to ethics, are 
the focus of most legislative attention. Then the chapter takes a deep 
dive into one of the world’s top public sector investors in AI, the United 
States, and studies how much its various government departments have 
spent on AI in the past five years.
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
CHAPTER HIGHLIGHTS
• 
An AI Index analysis of legislative records on AI in 25 countries shows that the number of bills 
containing “artificial intelligence” that were passed into law grew from just 1 in 2016 to 18 in 
2021. Spain, the United Kingdom, and the United States passed the highest number of AI-related 
bills in 2021, with each adopting three.
• 
The federal legislative record in the United States shows a sharp increase in the total number of 
proposed bills that relate to AI from 2015 to 2021, while the number of bills passed remains low, 
with only 2% ultimately becoming law.
• 
State legislators in the United States passed 1 out of every 50 proposed bills that contain AI 
provisions in 2021, while the number of such bills proposed grew from 2 in 2012 to 131 in 2021.
• 
In the United States, the current congressional session (the 117th) is on track to record the greatest 
number of AI-related mentions since 2001, with 295 mentions by the end of 2021, half way 
through the session, compared to 506 in the previous (116th) session.


5
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GLOBAL LEGISLATION  
RECORDS ON AI 
​
Governments and legislative bodies across the globe are 
increasingly seeking to pass laws to provide funding for 
AI development and innovation, while also promoting the 
integration of human-centered values. The AI Index has 
conducted an analysis of laws passed in 25 countries by 
their legislative bodies that contain the words “artificial 
intelligence” from 2016 to 2021.
Taken together, the 25 countries analyzed have passed a 
total of 55 AI-related bills. Figure 5.2.1 demonstrates that in 
the past six years, there has been a sharp increase in terms 
of the total number of AI-related bills passed into law.1
5.1 AI AND POLICYMAKING
1 Note that the analysis only includes laws passed by national legislative bodies (e.g.  congress, parliament) with the keyword “artificial intelligence” in various languages in the title or body of the bill 
text. See the appendix for the methodology. Countries included: Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, the Netherlands, New Zealand, 
Norway, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, the United Kingdom, and the United States.
Artificial Intelligence
Index Report 2022
Discussions around AI governance regulation have accelerated over the past decade, resulting in policy proposals across various 
legislative bodies. This section first examines AI-related legislation that has either been proposed or passed into law across different 
countries and regions, followed by a focused analysis of state-level legislation in the United States. It then takes a closer look at 
congressional and parliamentary records on AI across the world and concludes with data on the number of policy papers published 
in the United States.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2016
2017
2018
2019
2020
2021
0
5
10
15
Number of AI-Related Bills
18
NUMBER of AI-RELATED BILLS PASSED into LAW in 25 SELECT COUNTRIES, 2016–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.1


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By Geographic Area
Figure 5.1.2a shows the number of laws containing 
mentions of AI that were enacted in 2021. Spain, the 
United Kingdom, and the United States led, each passing 
three. Figure 5.1.2b shows the total number of legislation 
passed in the past six years. The United States dominated 
the list with 13 bills, starting in 2017 with 3 new laws 
passed each subsequent year, followed by Russia, 
Belgium, Spain, and the United Kingdom.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
0
1
2
3
4
Number of AI-Related Bills
Spain
United Kingdom
United States
Belgium
Russia
France
Germany
Italy
Japan
South Korea
3
3
3
2
2
1
1
1
1
1
NUMBER of AI-RELATED BILLS PASSED into LAW in SELECT COUNTRIES, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.2a
The United States 
dominated the list with 13 
bills, starting in 2017 with 
3 new laws passed each 
subsequent year, followed 
by Russia, Belgium, Spain, 
and the United Kingdom.


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Federal AI Legislation in the United States
A closer look at the federal legislative record in the 
United States shows a sharp increase in the total number 
of proposed bills that relate to AI (Figure 5.1.3). In 2015, 
just one federal bill was proposed, while in 2021, there 
were 130. Although this jump is significant, the number 
of bills related to AI being passed has not kept pace with 
the growing volume of proposed AI-related bills. This gap 
was most evident in 2021, when only 2% of all federal-
level AI-related bills were ultimately passed into law. 
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2015
2016
2017
2018
2019
2020
2021
0
20
40
60
80
100
120
Number of AI-Related Bills
130, Proposed
3, Passed
NUMBER of AI-RELATED BILLS in the UNITED STATES, 2015–21 (PROPOSED vs. PASSED)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.3
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Number of AI-Related Bills
United States
Russia
Belgium
Spain
United Kingdom
France
Italy
South Korea
Japan
China
Brazil
Canada
Germany
India
13
6
4
4
4
3
5
5
5
2
1
1
1
1
NUMBER of AI-RELATED BILLS PASSED into LAW in SELECT COUNTRIES, 2016–21 (SUM)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.2b


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5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
A Closer Look at the Legislation
The following subsection delves into some of the AI-related legislation passed into law since 2016. 
Table 5.1.1 demonstrates the wide range of AI-related issues that have piqued policymakers’ interest.
Country
Year Passed
Bill Name
Description
Canada
2017
Budget Implementation Act 2017, No. 1
A provision of this act authorized the Canadian 
government to make a payment of $125 million 
to the Canadian Institute for Advanced Research 
to support the development of a pan-Canadian 
artificial intelligence strategy. 
China
2019
Law of the People’s Republic of China 
on Basic Medical and Health Care and 
the Promotion of Health
A provision of this law aimed to promote the 
application and development of big data and 
artificial intelligence in the health and medical field 
while accelerating the construction of medical and 
healthcare information infrastructure, developing 
technical standards on the collection, storage, 
analysis, and application of medical and health data.
Russia
2020
Federal Law of 24 April 2020 No. 
123-FZ on the Experiment to Establish 
Special Regulation in order to Create 
the Necessary Conditions for the 
Development and Implementation of 
Artificial Intelligence Technologies in 
the Region of the Russian Federation 
– Federal City of Moscow and 
Amending the Articles 6 and 10 of the 
Federal Law on Personal Data
This law established an experimental framework 
for the development and implementation of AI as 
a five-year experiment to start in Moscow in July 
1, 2020, including allowing AI systems to process 
anonymized personal data for governmental and 
certain commercial business activities.
United Kingdom
2020
Supply and Appropriation (Main 
Estimates) Act 2020, c.13
A provision of this act authorized the Office of 
Qualifications and Examination Regulation to 
explore opportunities for using artificial intelligence 
to improve the marking and administration of high-
stakes qualifications.
United States
2020
IOGAN ACT: Identifying Outputs of 
Generative Adversarial Networks Act
This act directed the National Science Foundation 
to support research dedicated to studying the 
outputs of generative adversarial networks 
(deepfakes) and other comparable technologies.
Belgium
2021
Decree on coaching and solution-
oriented support for job seekers, N. 
327
A provision of this act directs the government 
to create an advisory group called the Ethics 
Committee, which is responsible for submitting 
advice if artificial intelligence tools are to be used 
for digitization activities.
France
2021
Law N:2021-1485 of November 
15, 2021, aimed at reducing the 
environmental footprint of digital 
technology in France
This act sets up a monitoring system to evaluate 
environmental impacts of newly emerging digital 
technologies, in particular, artificial intelligence. 
Table 5.1.1


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STATE-LEVEL AI LEGISLATION IN 
THE UNITED STATES
Growing policy interest in AI can also be seen in the 
large number of AI-related bills recently proposed 
at the state level in the United States, based on data 
provided by Bloomberg Government since 2012. 
Bloomberg Government classified a bill as relating to 
AI if it contained AI-related keywords such as artificial 
intelligence, machine learning, or algorithmic bias.
As is the case on the federal level, there has been a 
significant increase in the number of AI bills proposed 
at the state level in the last decade (Figure 5.1.4). 
In 2012, the first two pieces of AI-related legislation 
were proposed when New Jersey assembly member 
Annette Quijano directed the New Jersey Motor Vehicle 
Commission to establish driver’s license endorsements 
for autonomous vehicles. In the past 10 years, the 
increase has been substantial, from 2 bills in 2012 to 131 
in 2021.
A notable difference between AI-related lawmaking in the 
United States on the federal versus the state level is that 
a greater proportion of proposed state-level AI bills have 
actually passed. In 2021, of the 131 proposed state bills, 
26 were passed into law (20%), or 1 out of 5 proposed 
bills became law. This ratio is significantly higher when 
compared to the federal level, where 1 out of every 50 
proposed bills became law in 2021.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0
20
40
60
80
100
120
140
Number of AI-Related Bills
103
66
26
25
74
10
13
12
17
9
8
2
10
14
9
9
29
26
87
77
131
NUMBER of STATE-LEVEL AI-RELATED BILLS in the UNITED STATES, 2012–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.4
Passed
Proposed
Vetoed
A notable difference between 
AI-related lawmaking in the 
United States on the federal 
versus the state level is 
that a greater proportion of 
proposed state-level AI bills 
have actually passed.


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By State
In the United States, AI 
lawmaking has been relatively 
widespread across all states. As 
of 2021, 41 out of 50 states have 
proposed at least one AI-related 
bill, but certain states have been 
particularly active in generating 
AI legislation. Figure 5.1.5 shows 
that Massachusetts has proposed 
the most AI bills, with 40 since 
2012, followed by Hawaii (35) 
and New Jersey (32). Focusing 
on just 2021 in Figure 5.1.6, 
Massachusetts was the state that 
proposed the most AI-related 
bills, with 20, followed by Illinois 
(15) and Alabama (12).
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
MO
4
NM
1
MN
2
WA
14
MD
8
WV
9
MA
40
WY
0
CO
2
OH
3
MS
7
MT
0
ME
0
NC
6
NH
0
ND
0
OK
2
DC
8
GA
3
CA
29
OR
1
NV
10
NY
31
AK
0
TN
7
VA
8
NE
2
SC
1
CT
2
AZ
7
AR
1
SD
0
DE
1
NJ
32
PA
7
KY
1
WI
0
UT
3
KS
1
VT
8
LA
0
AL
21
MI
3
TX
17
HI
35
FL
22
IL
28
IN
1
ID
0
IA
1
RI
5
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED
STATES by STATE, 2012–21 (SUM)
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.5
MO
1
NM
0
MN
0
WA
6
MD
4
WV
3
MA
20
WY
0
CO
2
OH
1
MS
3
MT
0
ME
0
NC
3
NH
0
ND
0
OK
1
DC
6
GA
0
CA
4
OR
1
NV
1
NY
8
AK
0
TN
2
VA
1
NE
0
SC
1
CT
0
AZ
0
AR
0
SD
0
DE
0
NJ
4
PA
3
KY
0
WI
0
KS
0
UT
2
VT
3
LA
0
AL
12
MI
0
TX
6
FL
7
IN
0
HI
7
ID
0
IA
1
IL
15
RI
3
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED
STATES by STATE, 2021
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.6


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Sponsorship by Political Party
State-level AI legislation data reveals that there is a 
partisan dynamic to AI lawmaking. Figure 5.1.7 plots the 
number of AI-related bills sponsored at the state level by 
Democratic and Republican lawmakers. Although there 
has been an increase in AI bills proposed by members 
of both parties since 2012, in the past four years, the 
data suggests Democrats were more likely to sponsor 
AI-related legislation. Whereas Democrats sponsored 
only two more AI bills than Republicans in 2018, they 
sponsored 39 more in 2021.
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0
10
20
30
40
50
60
70
80
Number of AI-Related Bills
79, Democratic
40, Republican
NUMBER of STATE-LEVEL PROPOSED AI-RELATED BILLS in the UNITED STATES by SPONSOR PARTY, 2012–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.7
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE


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MENTIONS OF AI IN LEGISLATIVE 
RECORDS
Another barometer of legislative interest in AI is the 
number of mentions of “artificial intelligence” in 
governmental and parliamentary proceedings. This 
subsection considers data on mentions of AI both 
in U.S. congressional records and the parliamentary 
proceedings of other countries based on AI Index and 
Bloomberg Government data. 
AI Mentions in U.S. Congressional Records
In the last five years, and especially in 2021, U.S. 
congressional sessions have devoted increasing amounts 
of time to discussions of AI. This section presents data 
from Bloomberg Government concerning mentions of AI-
related keywords in congressional proceedings, broken 
down by legislation, congressional committee reports, 
and congressional research service reports.
According to Figure 5.1.8, the current congressional 
session (the 117th) is on track (as of the end of 2021) 
to record the greatest number of AI-related mentions 
since 2001. The most recently completed congressional 
session, the 116th (2019-2020), saw 506 AI mentions, 
nearly 3.4 times as many mentions as there were during 
the 115th session (2017–2018), and 30 times as many as 
the 114th session (2015–2016).
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
107th
(2001–02)
108th
(2003–04)
109th
(2005–06)
110th
(2007–08)
111th
(2009–10)
112th
(2011–12)
113th
(2013–14)
114th
(2015–16)
115th
(2017–18)
116th
(2019–20)
117th
(2021–)
0
100
200
300
400
500
Number of Mentions
245
139
129
178
66
44
39
83
27
4
7
25
17
18
17
12
17
149
506
295
MENTIONS of AI in the U.S. CONGRESSIONAL RECORD by LEGISLATIVE SESSION, 2001–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.1.8
Legislation
Congressional Research Service Reports
Committee Reports


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AI Mentions in Global Legislative Proceedings
AI mentions in governmental proceedings are on the 
rise not only in the United States but also in many other 
countries across the world. The AI Index conducted an 
analysis on the minutes or proceedings of legislative 
sessions in 25 countries that contain the keyword 
“artificial intelligence” from 2016 to 2021. Figure 5.1.9 
shows that the mentions of AI in legislative proceedings 
in 25 select countries grew 7.7 times in the past six years.2
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2016
2017
2018
2019
2020
2021
0
200
400
600
800
1,000
1,200
Number of Mentions
1,323
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in 25 SELECT COUNTRIES, 2016–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.9
2 See the appendix for the methodology. Countries included: Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, the Netherlands, New Zealand, 
Norway, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, the United Kingdom, and the United States.


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By Geographic Area
Figure 5.1.10a shows the number of legislative 
proceedings containing mentions of AI that were 
enacted in 2021. Similar to the trend in the number of 
AI mentions in bills passed into laws, Spain, the United 
Kingdom, and the United States topped the list. Figure 
5.1.2b shows the total number of AI mentions in the past 
six years. The United Kingdom dominated the list with 
939 mentions, followed by Spain, Japan, the United 
States, and Australia.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
0
50
100
150
200
250
300
Number of Mentions
Spain
United Kingdom
United States
Australia
Japan
Ireland
Brazil
Italy
Singapore
Belgium
Germany
France
Canada
Norway
Sweden
Finland
Russia
South Africa
Netherlands
India
New Zealand
South Korea
Denmark
Switzerland
269
185
132
122
60
46
64
20
95
25
76
47
22
72
10
16
12
12
11
6
6
3
5
7
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in
SELECT COUNTRIES, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.10a
0
200
400
600
800
1000
Number of Mentions
United Kingdom
Spain
Japan
United States
Australia
Singapore
Ireland
Italy
Germany
France
Brazil
Belgium
Canada
Finland
Sweden
Netherlands
Russia
Norway
India
South Africa
Denmark
New Zealand
South Korea
Switzerland
466
939
559
422
282
222
410
164
120
158
155
123
34
58
33
52
67
111
78
27
27
15
21
71
NUMBER of MENTIONS of AI in LEGISLATIVE PROCEEDINGS in
SELECT COUNTRIES, 2016–2021 (SUM)
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.10b


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U.S. AI POLICY PAPERS
To estimate activities outside national governments 
that are also informing AI-related rulemaking, the 
AI Index tracks 55 U.S.-based organizations that 
published policy papers in the past four years. Those 
organizations include: think tanks and policy institutes 
(19); university institutes and research programs (14); 
civil society organizations, associations, and consortiums 
(9); industry and consultancy organizations (9); and 
government agencies (4).3 A policy paper in this section 
is defined as a research paper, research report, brief, or 
blog post that addresses issues related to AI and makes 
specific recommendations to policymakers. Topics of 
those papers are divided into primary and secondary 
categories: A primary topic is the main focus of the paper, 
while a secondary topic is a subtopic of the paper or an 
issue that was briefly explored.
Figure 5.1.11 plots the total number of U.S.-based AI-
related policy papers that have been published from 
2018 to 2021, which can proxy the general interest in AI 
within the U.S. policymaking space. The total number of 
policy papers has tripled since 2018, peaking in 2020 with 
273, and decreasing slightly in 2021, with 210.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
2018
2019
2020
2021
0
50
100
150
200
250
Number of Policy Papers
210
NUMBER of AI-RELATED POLICY PAPERS by U.S.-BASED ORGANIZATIONS, 2018–21
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Figure 5.1.11
3 The complete list of organizations the Index followed can be found in the Appendix.


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By Topic
In 2021, the leading primary topics were Privacy, Safety, 
and Security; Innovation and Technology; and Ethics 
(Figure 5.1.12). Certain topics, such as government and 
public administration, education and skills, as well as 
democracy, did not feature prominently as primary 
topics, but they were reported on more frequently 
as secondary topics. Among the AI topics to receive 
comparatively little attention from tracked organizations 
are those that relate to energy and the environment, 
humanities, physical sciences, and social and behavioral 
sciences.
5.1 AI and Policymaking
CHAPTER 5: AI POLICY AND GOVERNANCE
Primary Topic
Secondary Topic
0
20
40
60
0
20
40
60
Privacy, Safety, and Security
Innovation and Technology
Ethics
Int'l A"airs and Int'l Security
Industry and Regulation
Equity and Inclusion
Workforce and Labor
Gov't and Public Administration
Justice and Law Enforcement
Education and Skills
Communications and Media
Health and Biological Sciences
Social and Behavioral Sciences
Democracy
Physical Sciences
Energy and Environment
Humanities
36
59
34
29
62
62
33
23
51
51
15
0
4
2
7
1
1
30
63
36
45
45
29
58
58
57
13
51
17
17
3
3
1
1
NUMBER of AI-RELATED POLICY PAPERS by U.S.-BASED ORGANIZATIONS by TOPIC, 2021
Source: AI Index, 2021 | Chart: 2022 AI Index Report
Number of Policy Papers
Figure 5.1.12


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FEDERAL BUDGET FOR NONDEFENSE  
AI R&D 
In December 2021, the National Science and Technology Council 
published a report on the public-sector AI R&D budget across 
departments and agencies participating in the Networking and 
Information Technology Research and Development (NITRD) 
program and the National Artificial Intelligence Initiative. The report 
does not include information on classified AI R&D investment by the 
defense and intelligence agencies.
In fiscal year (FY) 2021, nondefense U.S. government agencies 
allocated a total of $1.53 billion to AI R&D spending, approximately 
2.7 times what was spent in FY 2018 (Figure 5.2.1). This figure 
is projected to rise 8.8% for FY 2022, with a total of $1.67 billion 
requested.4 The increasing amount spent on AI R&D by nondefense 
departments indicates the U.S. government’s continued strong 
interest in public sector funding for AI research and development 
spanning a wide range of federal agencies.
5.2 U.S. PUBLIC INVESTMENT IN AI 
4 See NITRD website for details on AI R&D investment FY 2018-22 with the breakdown of core AI vs AI crosscut. Note that AI crosscutting budget data is not available for FY 2018.
Artificial Intelligence
Index Report 2022
This section examines the public AI investment in the United States, based on data from the U.S. government and Bloomberg Government.
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE
FY18 (ENACTED)
FY19 (ENACTED)
FY20 (ENACTED)
FY21 (ENACTED)
FY22 (REQUESTED)
0.00
0.50
1.00
1.50
Budget (in billions of U.S. Dollars)
0.56
1.43
1.53
1.67
1.11
U.S. FEDERAL BUDGET for NONDEFENSE AI R&D, FY 2018–22
Source: U.S. NITRD Program, 2022 | Chart: 2022 AI Index Report
Figure 5.2.1
The increasing amount 
spent on AI R&D by 
nondefense departments 
indicates the U.S. 
government’s continued 
strong interest in 
public sector funding 
for AI research and 
development spanning 
a wide range of federal 
agencies.


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U.S. DEPARTMENT OF DEFENSE 
BUDGET REQUEST
Spending on AI by the U.S. Department of Defense (DOD) 
can be proxied by looking at the publicly available 
requests made by the DOD for research, development, 
test, and evaluation (RDT&E) relating to AI. In FY 2021, 
DOD allocated $9.26 billion across 500 AI R&D programs 
(Figure 5.2.2), a 6.68% increase from the $8.68 billion 
spent in 2020. For FY 2022, the department has requested 
$10 billion so far, which is likely to grow once additional 
requests and congressional appropriations are taken into 
account.
Important data caveat: This chart is indicative of one 
of the challenges of quantifying public AI spending. 
Bloomberg Government’s analysis that searches AI-
relevant keywords in DOD budgets shows that the 
department is requesting $10.0 billion for AI-specific R&D 
in FY 2022. However, DOD’s own measurement produces 
a smaller number of $874 million. The discrepancy 
may result from the difference in defining AI-related 
budget items. For example, a research project that uses 
AI for cyber defense may count human, hardware, and 
operations-related expenditures within the AI-related 
budget request, though the AI software component will 
be much smaller.
Sum of FY20 Funding
Sum of FY21 Funding
Sum of FY22 Funding
0
2
4
6
8
10
Budget (in billions of U.S. Dollars)
10.00
8.68
9.26
0.84: DOD Reported Budget on AI R&D
0.93: DOD Reported Budget on AI R&D
0.87: DOD Reported Budget on AI R&D
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH, DEVELOPMENT, TEST and EVALUATION (RDT&E), FY 2020–22
Source: Bloomberg Government and U.S. Department of Defense, 2021 | Chart: 2022 AI Index Report
Figure 5.2.2
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
DOD Top Five Highest-Funded Programs
This section highlight offers a more qualitative look at some of the AI-related research projects the 
DOD prioritizes. Table 5.2.1 presents the five DOD-related AI programs that received the greatest 
funding in 2021. In the past year, the DOD was interested in deploying AI for a number of purposes, 
from geospatial monitoring to reducing the threat posed by weapons of mass destruction. 
Program Name
Department
Funds Received 
(in millions)
Purpose
1 Rapid Capability 
Development and Maturation
Army
257 
Fund the development, engineering, acquisition, 
and operation of various AI-related technological 
prototypes that could be used for military purposes. 
2 Counter Weapons of 
Mass Destruction Advanced 
Technology Development
Defense Threat 
Reduction 
Agency
254 
Develop technologies that could “deny, defeat and 
disrupt” weapons of mass destruction (WMD).
3 Algorithmic Warfare  
Cross-Functional Teams –
Software Pilot Program 
Office of the 
Secretary of 
Defense
230 
Accelerate the integration of AI technologies in DOD 
systems to “improve warfighting speed and lethality.”
4 Joint Artificial Intelligence 
Center
Defense 
Information 
Systems 
Agency
137 
Develop, test, prototype, and demonstrate various AI 
and machine learning capabilities with the intention 
of integrating these capabilities across numerous 
domains which include “supply chain, personal 
recovery, infrastructure assessment, geospatial 
monitoring during disaster and cyber sense making.”
5 High Performance 
Computing Modernization 
Program
Army
96 
Investigate, demonstrate, and mature both general 
and special-purpose supercomputing environments 
that are used to satisfy wide-ranging DOD priorities.
Table 5.2.1
5.2 U.S. Public Investment in AI


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DOD AI R&D Spending by Department
DOD spending on AI R&D can also be broken down on 
a subdepartmental level, which reveals how individual 
defense agencies—the Army and the Navy, for instance—
compare in their AI spending (Figure 5.2.3). The U.S. 
Navy was the top-spending DOD agency in FY 2021 and 
is poised to maintain that position in 2022. They have 
requested a total of $1.86 billion in FY 2022 for AI-related 
projects, followed by the Army ($1.77 billion), the Office 
of the Secretary of Defense ($1.1 billion) and the Air Force 
($883 million).
FY20 (ENACTED)
FY21 (ENACTED)
FY22 (REQUESTED)
0
2
4
6
8
10
Budget (in billions of U.S. Dollars)
1.00
1.64
1.86
1.93
1.63
1.54
1.92
1.52
1.75
1.57
1.72
1.77
1.19
1.16
1.18
1.13
1.12
1.71
U.S. DOD BUDGET for AI-SPECIFIC RESEARCH, DEVELOPMENT, TEST and EVALUATION (RDT&E) by
DEPARTMENT, FY 2020–22
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Air Force
Army
DARPA
DISA
Navy
OSD
Other
Figure 5.2.3
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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U.S. GOVERNMENT AI-RELATED 
CONTRACT SPENDING
Public investment in AI can also be measured by federal 
government spending on AI-related contracts. U.S. 
government agencies often award contracts to private 
companies for the supply of various goods and services 
that typically occupy the largest share of an agency’s 
budget. Bloomberg Government built a model to classify 
whether a U.S. government contract was AI-related by 
adding up all contracting transactions that contain a set 
of more than 100 AI-specific keywords in their titles or 
descriptions.5
Total Contract Spending
In 2021, federal departments and agencies spent a total of 
$1.79 billion on AI-related contracts. Although this amount 
is nearly double what was spent on AI-related contracts in 
2018 (roughly $920 million), it represents a slight decrease 
from the amount spent on AI-related contracts in 2020, 
which peaked at $1.97 billion (Figure 5.2.4).
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
0.0
0.5
1.0
1.5
2.0
Contract Spending (in billions of U.S. Dollars)
1.79
U.S. GOVERNMENT TOTAL CONTRACT SPENDING on AI, FY 2000–21
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.4
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE
5 Note that contractors may add a number of keywords into their applications during the procurement process, so some of the projects included may have a relatively small AI component relative to 
other parts of technology.


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Contract Spending by Department and Agency
Figures 5.2.5 and 5.2.6 report AI-related contract spending 
by the top 10 federal agencies in 2021 and from 2000 to 
2021, respectively. The DOD outspent the rest of the U.S. 
government on both charts by a significant margin. In 
2021, it spent $1.14 billion on AI-related contracts, roughly 
five times what was spent by the next highest department, 
the Department of Health and Human Services ($234 
million). 
Aggregate spending on AI contracts in the last four years 
tells a similar story. Since 2018, the DOD has spent $5.20 
billion on AI contracts, approximately seven times the next 
highest spender, NASA ($1.41 billion). In fact, since 2018, 
the DOD has spent twice as much on AI-related contracts 
as all other government agencies combined. Following the 
DOD and NASA are the Department of Health and Human 
Services ($700 million), the Department of Homeland 
Security ($362 million), and Department of the Treasury 
($156 million). 
0
200
400
600
800
1000
1200
Contract Spending (in millions of U.S. Dollars)
Department of Defense (DOD)
Department of Health and Human Services (HHS)
National Aeronautics and Space Administration (NASA)
Department of Homeland Security (DHS)
Department of Commerce (DOC)
Department of the Treasury (TREAS)
Department of Veterans A"airs (VA)
Department of Transportation (DOT)
Securities and Exchange Commission (SEC)
Department of Agriculture (USDA)
Department of Energy (DOE)
Agency for International Development (USAID)
Department of Justice (DOJ)
Department of State (DOS)
National Science Foundation (NSF)
1,138
234
159
49
38
25
81
12
12
12
6
4
8
3
2
TOP CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2021
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.5
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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0
1
2
3
4
5
Contract Spending (in billions of U.S. Dollars)
Department of Defense (DOD)
National Aeronautics and Space Administration (NASA)
Department of Health and Human Services (HHS)
Department of Homeland Security (DHS)
Department of the Treasury (TREAS)
Department of Veterans A!airs (VA)
Department of Commerce (DOC)
Department of Agriculture (USDA)
Department of Energy (DOE)
Securities and Exchange Commission (SEC)
General Services Administration (GSA)
Department of State (DOS)
Social Security Administration (SSA)
Department of Transportation (DOT)
0.06
0.06
0.06
0.06
0.05
0.05
0.45
0.07
0.70
0.32
5.20
0.15
0.15
1.41
TOP CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2000–21 (SUM)
Source: Bloomberg Government, 2021 | Chart: 2022 AI Index Report
Figure 5.2.6
5.2 U.S. Public Investment in AI
CHAPTER 5: AI POLICY AND GOVERNANCE


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CHAPTER 5: AI POLICY AND GOVERNANCE
Largest Contract for Five Top-Spending  
Departments in 2021
To paint a better picture of how different U.S. government departments use AI, Table 5.2.2 shows the 
most expensive AI-related contract that the five highest AI-related-spending departments signed in 
2021. Last year, the U.S. government invested in AI to build autonomous vehicle prototypes, develop an 
AI imaging system that could assist with burn classification, and create robots capable of higher-level 
lunar navigation.
Contract Name
Department
Amount 
(in millions)
Purpose
Prototype Services in the Objective 
Areas of Automotive Cybersecurity, 
Vehicle Safety Technologies, Vehicle 
Light Weighting, Autonomous Vehicles 
and Intelligent Systems, Connected 
Vehicles, and Advanced Energy Storage 
Technologies
DOD
70 
To acquire prototypes in the domain of 
automotive cybersecurity, vehicle safety 
technologies, and autonomous vehicles and 
intelligent systems.
Biomedical Advanced Research and 
Development Authority (BARDA)
HHS
20 
To develop optical imaging devices and 
machine learning algorithms to assist 
in classifying and healing wounds and 
conventional burns. 
Commercial Lunar Payload Services
NASA
14
To develop lunar robots capable of navigating 
the moon’s south pole to acquire lunar 
resources and engage in lunar-based scientific 
activities. 
SBIR-Autonomous Surveillance  
Towers-Delivery Order
DHS
37
To construct towers capable of autonomous 
surveillance. 
Schedule 70: Information Technology 
DOC
13
To develop a prototype using AI technology 
that can improve patent search. 
Table 5.2.2
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APPENDIX
BLOOMBERG GOVERNMENT
Prepared by Amanda Allen
Bloomberg Government is a premium, subscription-
based service that provides comprehensive information 
and analytics for professionals who interact with—or 
are affected by—the government. Delivering news, 
analytics, and data-driven decision tools, Bloomberg 
Government’s digital workspace gives an intelligent edge 
to government affairs and contracting professionals. For 
more information or a demo, visit about.bgov.com.
 
Methodology
Contract Spending: Bloomberg Government’s Contracts 
Intelligence Tool structures all contracts data from 
www.fpds.gov. The CIT includes a model of government 
spending on artificial intelligence-related contracts that is 
based on a combination of government-defined product 
service codes and more than 100 AI-related keywords. 
For the section “U.S. Government Contract Spending,” 
Bloomberg Government analysts used contract spending 
data from fiscal year 2000 through fiscal year 2021.
Defense RDT&E Budget: Bloomberg Government 
organized all the RDT&E budget request line items 
available from the Defense Department Comptroller. For 
the section “U.S. Department of Defense (DOD) Budget,” 
Bloomberg Government used a set of AI-specific keywords 
to identify 500 unique budget activities related to artificial 
intelligence and machine learning worth a combined $5.9 
billion in FY 2021.
Legislative Documents: Bloomberg Government 
maintains a repository of congressional documents, 
including bills, Congressional Budget Office assessments, 
and reports published by congressional committees, 
the Congressional Research Service, and other offices. 
Bloomberg Government also ingests state legislative 
bills. For the section “AI Policy and Governance,” 
Bloomberg Government analysts identified all legislation, 
congressional committee reports, and CRS reports that 
referenced one or more AI-specific keywords.
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GLOBAL LEGISLATION RECORDS ON AI
For AI-related bills passed into laws, the AI Index performed searches of the keyword “artificial intelligence,” in respective 
languages, on the websites of 25 countries’ congresses or parliaments, in full-text of bills. Note that only laws passed 
by state-level legislative bodies and signed into law (i.e., by presidents or received royal assent) from 2015 to 2021 are 
included. Future AI Index reports hope to include analysis on other types of legal documents, such as regulations and 
standards, adopted by state- or supranational-level legislative bodies, government agencies, etc.
Denmark
Website: https://www.retsinformation.dk/ 
Keyword: kunstig intelligen
Filter:
	
• Document Type: Laws
Finland
Website: https://www.finlex.fi/
Keyword: tekoäly 
Noting under the Current Legislation section
France 
Website: https://www.legifrance.gouv.fr/ 
Keyword: intelligence artificielle
Filter: 
	
• texte consolidé
	
• Document Type: Law
Germany
Website: http://www.gesetze-im-internet.de/index.html 
Keyword: künstliche Intelligenz
Filter: 
	
• 
All federal codes, statutes, and ordinances that are 
currently in force
	
• 
Volltextsuche (full text)
	
• 
Und-Verknüpfung der Wörter (entire word)
India 
Website: https://www.indiacode.nic.in 
Keyword: artificial intelligence
Note: The website used allows for a search of keywords 
in legalization title but not in the full text, as such it is not 
useful for this particular research. Therefore, a Google 
search using the “site” function to search the site with the 
keyword of “artificial intelligence” is conducted.
Australia
Website: www.legislation.gov.au 
Keyword: artificial Intelligence
Filters: 
	
• Legislation types: Acts
	
• 
Portfolios: Department of House of Representatives, 
Department of Senate
Note: Texts in explanatory memorandum are not counted.
Belgium
Website: http://www.ejustice.just.fgov.be/loi/loi.htm  
Keyword: intelligence artificielle
Brazil
Website: https://www.camara.leg.br/legislacao  
Keyword: inteligência artificial
Filter:
	
• Federal legislation
	
• Type: Law
Canada
Website: https://www.parl.ca/legisinfo/ 
Keyword: artificial Intelligence
Note: Results were investigated to determine how many of 
the bills introduced were eventually passed (i.e., received 
royal assent) and bill status was recorded.
China
Website: https://flk.npc.gov.cn/  
Keyword: 人工智能
Filters: 
	
• 
Legislative body: Standing Committee of the 
National People’s Congress
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Ireland
Website: www.irishstatutebook.ie
Keyword: artificial intelligence
Italy
Website: https://www.normattiva.it/
Keyword: intelligenza artificiale
Filter: 
	
• 
Document Type: law
Japan
Website: https://elaws.e-gov.go.jp/
Keyword: 人工知能
Filter:
	
• 
Full text
	
• 
Law
Netherlands
Website: https://www.overheid.nl/ 
Keyword: kunstmatige intelligentie
Filter:
	
• 
Document Type: Wetten
New Zealand
Website: www.legislation.govt.nz
Keyword: Artificial intelligence
Filter: 
	
• 
Document type: acts
	
• 
Status option: For the status option (example: acts in 
force, current bills, etc.) 
Norway
Website: https://lovdata.no/
Keyword: kunstig intelligens
Russia
Website: http://graph.garant.ru:8080/SESSION/PILOT/
main.htm (Database “The Federal Laws” in the official 
website of the Federation Council of the Federal Assembly 
of the Russian Federation.)
Keyword: искусственный интеллект
Filter:
	
• 
Words in text
Singapore
Website: https://sso.agc.gov.sg/
Keyword: artificial intelligence
Filter: 
	
• 
Document Type: Current acts and subsidiary 
legislation
South Africa
Website: www.gov.za
Keyword: artificial intelligence
Filter: 
	
• 
Document: acts
Note: This search function seemingly does not search 
within the context of the full text and so no results were 
returned. Therefore, a Google search using the “site” 
function to search the site with the keyword of “artificial 
intelligence” is conducted. 
South Korea
Website: https://law.go.kr/eng/; https://elaw.klri.re.kr/
Keyword: artificial Intelligence or 인공 지능
Filter:
	
• 
Type: Act 
Note: Cannot search combined words, so individual 
analysis is conducted. 
Spain 
Website: https://www.boe.es/ 
Keyword: inteligencia artificial
Filter:
	
• 
Type: law
	
• 
Head of state (for passed laws)
Sweden
Website: https://www.riksdagen.se/
Keyword: artificiell intelligens
Filter: Swedish Code of Statutes
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Switzerland
Website: https://www.fedlex.admin.ch/ 
Keyword: intelligence artificielle
Filter: 
	
• 
Text category: federal constitution, federal acts, and 
federal decrees, miscellaneous texts, orders, and 
other forms of legislation.
	
• 
Publication period for legislation was limited to 
2015-2021.  
United Kingdom
Website: https://www.legislation.gov.uk/
Keyword: artificial intelligence
Filter:
	
• 
Legislation Type: U.K. Public General Acts & U.K. 
Statutory Instruments
United States
Website: https://www.congress.gov/ 
Keyword: artificial intelligence
Filter:
	
• 
Source: Legislation
Status of legislation: Became law
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MENTIONS OF AI IN AI-RELATED LEGISLATION PROCEEDINGS
For mentions of AI in AI-related legislative proceedings around the world, the AI Index performed searches of the keyword 
“artificial intelligence,” in respective languages, on the websites of 25 countries’ congresses or parliaments, usually under 
sections named “minutes,” “hansard,” etc.
Denmark
Website: https://www.retsinformation.dk/ 
Keyword: kunstig intelligens
Filter:
	
• Minutes
Finland
Website: https://www.eduskunta.fi/ 
Keyword: tiedot
Filter:
	
• Parliamentary Affairs and Documents
	
• Public document: Minutes
	
• Actor: Plenary sessions
France 
Website: https://www.assemblee-nationale.fr/ 
Keyword: intelligence artificielle
Filter: 
	
• Reports of the debates in session
Note: Such documents were only prepared starting in 
2017.
Germany
Website: https://dip.bundestag.de/ 
Keyword: künstliche Intelligenz
Filter: 
	
• Speeches, requests to speak in the plenum
India 
Website: http://loksabhaph.nic.in/ 
Keyword: artificial intelligence
Filter:
	
• Exact word/phrase
Ireland
Website: https://www.oireachtas.ie/ 
Keyword: artificial intelligence
Filter: Content of parliamentary debates
Australia
Website: https://www.aph.gov.au/Parliamentary_Business/
Hansard 
Keyword: artificial intelligence
Belgium
Website: http://www.parlement.brussels/search_form_fr/ 
Keyword: intelligence artificielle
Filter
	
• Document Type: all
Brazil
Website: https://www2.camara.leg.br/atividade-legislativa/
discursos-e-notas-taquigraficas  
Keyword: inteligência artificial
Filter:
	
• Federal legislation
	
• Type: Law
Canada
Website: https://www.ourcommons.ca/PublicationSearch/
en/?PubType=37 
Keyword: artificial Intelligence
China
Website: Various reports on the work of the government
Keyword: 人工智能
Note: The National People’s Congress is held once per 
year and does not provide full legislative proceedings. 
Hence, the counts included in the analysis only searched 
the mentions of artificial intelligence in the only public 
document released from the Congress meetings, the 
Report on the Work of the Government, delivered by the 
Premier. 
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Italy
Website: https://aic.camera.it/aic/search.html 
Keyword: intelligenza artificiale
Filter: 
	
• Type: All
	
• Search by exact phrase
Japan
Website: https://kokkai.ndl.go.jp/#/  
Keyword: 人工知能
Filter:
	
• Full text
	
• Law
Netherlands
Website: https://www.tweedekamer.nl/kamerstukken?pk_
campaign=breadcrumb  
Keyword: kunstmatige intelligentie
Filter:
	
• Parliamentary papers - Plenary reports
New Zealand
Website: https://www.parliament.nz/en/pb/hansard-
debates/  
Keyword: artificial intelligence 
Norway
Website: https://www.stortinget.no/no/Saker-og-
publikasjoner/Publikasjoner/Referater/ 
Keyword: kunstig intelligens
Note: This search function does not directly allow the 
keyword within minutes. Therefore, a Google search using 
the “site” function to search the site with the keyword of 
“artificial intelligence” is conducted. 
Russia
Website: http://transcript.duma.gov.ru/ 
Keyword: искусственный интеллект
Filter:
	
• Words in text
Singapore
Website: https://sprs.parl.gov.sg/search/home 
Keyword: artificial intelligence
South Africa
Website: https://www.parliament.gov.za/hansard 
Keyword: artificial intelligence
Note: This search function does not search within the 
context of the full text and so no results were returned. 
Therefore, a Google search using the “site” function 
to search https://www.parliament.gov.za/storage/
app/media/Docs/hansard/ with the keyword “artificial 
intelligence” is conducted. 
South Korea
Website: http://likms.assembly.go.kr/ 
Keyword: 인공 지능
Filter:
	
• Meeting Type: All
Spain 
Website: https://www.congreso.es/ 
Keyword: inteligencia artificial
Filter:
	
• Official publications of parliamentary proceedings
Switzerland
Website: https://www.parlament.ch/ 
Keyword: intelligence artificielle
Filter: 
	
• Parliamentary proceedings
Sweden
Website: https://www.riksdagen.se/sv/global/
sok/?q=&doktyp=prot
Keyword: artificiell intelligens
Filter:
	
• Minutes
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United Kingdom
https://hansard.parliament.uk/ 
Keyword: artificial intelligence
Filter
	
• References
United States
Website: https://www.congress.gov/ 
Keyword: artificial intelligence
Filter:
	
• Source: Congressional record
	
• 
Congressional record section: Senate, House of 
Representatives, and Extensions of Remarks
U.S. AI POLICY PAPERS
Organizations
To develop a more nuanced understanding of the thought 
leadership that motivates AI policy, we tracked policy 
papers published by 55 organizations in the United States 
or with a strong presence in the United States (expanded 
from the list of 36 organizations last year) across four 
broad categories: 
	
• 
Civil Society, Associations & Consortiums: 
Algorithmic Justice League, Alliance for Artificial 
Intelligence in Healthcare, Amnesty International, 
EFF, Future of Privacy Forum, Human Rights Watch, 
IJIS Institute, Institute for Electrical and Electronics 
Engineers, Partnership on AI
	
• 
Consultancy: Accenture, Bain & Company, Boston 
Consulting Group, Deloitte, McKinsey & Company
	
• 
Government Agencies: Congressional Research 
Service, Library of Congress, Defense Technical 
Information Center, Government Accountability 
Office, Pentagon Library
	
• 
Private Sector Companies: Google AI, Microsoft AI, 
Nvidia, OpenAI
	
• 
Think Tanks & Policy Institutes: American Enterprise 
Institute, Aspen Institute, Atlantic Council, Brookings 
Institute, Carnegie Endowment for International 
Peace, Cato Institute, Center for a New American 
Security, Center for Strategic and International 
Studies, Council on Foreign Relations, Heritage 
Foundation, Hudson Institute, MacroPolo, National 
Security Institute, New America Foundation, RAND 
Corporation, Rockefeller Foundation, Stimson 
Center, Urban Institute, Wilson Center
	
• 
University Institutes & Research Programs: AI and 
Humanity Cornell University; AI Now Institute, 
New York University; AI Pulse, UCLA Law; Belfer 
Center for Science and International Affairs, 
Harvard University; Berkman Klein Center, Harvard 
University; Center for Information Technology 
Policy, Princeton University; Center for Long-Term 
Cybersecurity, UC Berkeley; Center for Security 
and Emerging Technology, Georgetown University; 
CITRUS Policy Lab, UC Berkeley; Hoover Institution; 
Institute for Human-Centered Artificial Intelligence, 
Stanford University; Internet Policy Research 
Initiative, Massachusetts Institute of Technology; 
MIT Lincoln Laboratory; Princeton School of Public 
and International Affairs
Methodology
Each broad topic area is based on a collection of 
underlying keywords that describe the content of the 
specific paper. We included 17 topics that represented the 
majority of discourse related to AI between 2018-2021. 
These topic areas and the associated keywords are listed 
below:
	
• 
Health & Biological Sciences: medicine, healthcare 
systems, drug discovery, care, biomedical research, 
insurance, health behaviors, COVID-19, global health
	
• 
Physical Sciences: chemistry, physics, astronomy, 
earth science
	
• 
Energy & Environment: energy costs, climate 
change, energy markets, pollution, conservation, oil 
and gas, alternative energy
	
• 
International Affairs & International Security: 
international relations, international trade, 
developing countries, humanitarian assistance, 
warfare, regional security, national security, 
autonomous weapons
	
• 
Justice & Law Enforcement: civil justice, criminal 
justice, social justice, police, public safety, courts
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• 
Communications & Media: social media, 
disinformation, media markets, deepfakes
	
• 
Government & Public Administration: federal 
government, state government, local government, 
public sector efficiency, public sector effectiveness, 
government services, government benefits, 
government programs, public works, public 
transportation
	
• 
Democracy: elections, rights, freedoms, liberties, 
personal freedoms
	
• 
Industry & Regulation: economy, antitrust, M&A, 
competition, finance, management, supply chain, 
telecom, economic regulation, technical standards, 
autonomous vehicle industry and regulation
	
• 
Innovation & Technology: advancements and 
improvements in AI technology, R&D, intellectual 
property, patents, entrepreneurship, innovation 
ecosystems, startups, computer science, engineering
	
• 
Education & Skills: early childhood, K-12, higher 
education, STEM, schools, classrooms, reskilling
	
• 
Workforce & Labor: labor supply and demand, talent, 
immigration, migration, personnel economics, future 
of work
	
• 
Social & Behavioral Sciences: sociology, linguistics, 
anthropology, ethnic studies, demography, 
geography, psychology, cognitive science
	
• 
Humanities: arts, music, literature, language, 
performance, theater, classics, history, philosophy, 
religion, cultural studies
	
• 
Equity & Inclusion: biases, discrimination, gender, 
race, socioeconomic inequality, disabilities, 
vulnerable populations
	
• 
Privacy, Safety & Security: anonymity, GDPR, 
consumer protection, physical safety, human control, 
cybersecurity, encryption, hacking
	
• 
Ethics: transparency, accountability, human 
values, human rights, sustainability, explainability, 
interpretability, decision-making norms


1 
 
 
 
 
 
 
 
On July 20, China's State Council issued a seminal document, entitled A Next 
Generation Artificial Intelligence Development Plan. This important aspirational document 
sets out a top-level design blueprint charting the country's approach to developing artificial 
intelligence (AI) technology and applications, setting broad goals up to 2030. 
  
Please find the full text of the document below. 
 
The translators produced analysis on the new document and Chinese AI ambitions for New 
America here. 
  
The document has been translated into English by a group of experienced Chinese 
linguists with deep backgrounds on the subject matter and on China's S&T establishment 
and current AI capabilities.  They are: Rogier Creemers, Leiden Asia Centre; Graham 
Webster, Yale Law School Paul Tsai China Center; Paul Triolo, Eurasia Group; and Elsa Kania. 
 
The group is grateful to New America Cybersecurity Initiative Fellow John Costello for 
comments that helped to improve the translation. 
   
Any errors in translation are the responsibility of the translators, and we welcome 
comments, which can be directed to the collaborators at this 
address: chinacomments@newamerica.org 
 
 
 


2 
State Council Notice on the Issuance of the Next 
Generation Artificial Intelligence Development Plan 
Completed: July 8, 2017 
Released: July 20, 2017 
 
A Next Generation Artificial  
Intelligence Development Plan 
 
The rapid development of artificial intelligence (AI) will profoundly change human society 
and life and change the world. To seize the major strategic opportunity for the development 
of AI, to build China’s first-mover advantage in the development of AI, to accelerate the 
construction of an innovative nation and global power in science and technology, in 
accordance with the requirements of the CCP Central Committee and the State Council, this 
plan has been formulated. 
I.  The Strategic Situation 
 
The development of AI has entered a new stage. After sixty years of evolution, especially in 
mobile Internet, big data, supercomputing, sensor networks, brain science, and other new 
theories and new technologies, under the joint impetus of powerful demands of economic 
and social development, AI’s development has accelerated, displaying deep learning, 
cross-domain integration, man-machine collaboration, the opening of swarm intelligence, 
autonomous control, and other new characteristics. Big data-driven cognitive learning, 
cross-media collaborative processing, and man-machine collaboration–strengthened 
intelligence, swarm integrated intelligence, and autonomous intelligent systems have 
become the focus of the development of AI. The results of brain science research inspired 
human-like intelligence that awaits action; the trends involving the chips, hardware, and 
platform have become apparent; the development of AI has entered into a new stage. At 
present, the development a new generation of AI and related disciplines, theoretical 
modeling, technological innovation, hardware and software upgrades, etc., all advance, 
provoking chain-style breakthroughs, promoting the acceleration of the elevation of 
economic and social domains from digitization and networkization to intelligentization.  
 
AI has become a new focus of international competition. AI is a strategic technology that 
will lead in the future; the world’s major developed countries are taking the development of 
AI as a major strategy to enhance national competitiveness and protect national security; 
intensifying the introduction of plans and strategies for this core technology, top talent, 
standards and regulations, etc.; and trying to seize the initiative in the new round of 
international science and technology competition. At present, China’s situation in national 
security and international competition is more complex, and [China] must, looking at the 
world, take the development of AI to the national strategic level with systemic layout, take 
the initiative in planning, firmly seize the strategic initiative in the new stage of 
international competition in AI development, to create new competitive advantage, 
opening up the development of new space, and effectively protecting national security. 
 
AI has become a new engine of economic development. AI has become the core driving 
force for a new round of industrial transformation, [which] will advance the release of the 


3 
huge energy stored from the previous scientific and technological revolution and industrial 
transformation, and create a new powerful engine, reconstructing production, distribution, 
exchange, consumption, etc., links in economic activities; with new demands taking shape 
from the macro to the micro within each domain of intelligentization; with the birth of new 
technologies, new products, new industries, new formats, new models; triggering 
significant changes in economic structure, profound changes in human modes of 
production, lifestyle, and thinking; and a whole leap of achieving social productivity. 
China’s economic development enters a new normal, deepening the supply side of 
structural reform task is very arduous, [and China] must accelerate the rapid application of 
AI, cultivating and expanding AI industries to inject new kinetic energy into China’s 
economic development. 
 
AI brings new opportunities for social construction. China is currently in the decisive stage 
of comprehensively constructing a moderately prosperous society. The challenges of 
population aging, environmental constraints, etc., remain serious. The widespread use of AI 
in education, medical care, pensions, environmental protection, urban operations, judicial 
services, and other fields will greatly improve the level of precision in public services, 
comprehensively enhancing the people’s quality of life. AI technologies can accurately 
sense, forecast, and provide early warning of major situations for infrastructure facilities 
and social security operations; grasp group cognition and psychological changes in a 
timely manner; and take the initiative in decision-making and reactions—which will 
significantly elevate the capability and level of social governance, playing an irreplaceable 
role in effectively maintaining social stability. 
 
The uncertainties in the development of AI create new challenges. AI is a disruptive 
technology with widespread influence that may cause: transformation of employment 
structures; impact on legal and social theories; violations of personal privacy; challenges in 
international relations and norms; and other problems. It will have far-reaching effects on 
the management of government, economic security, and social stability, as well as global 
governance. While vigorously developing AI, we must attach great importance to the 
potential safety risks and challenges, strengthen the forward-looking prevention and 
guidance on restraint, minimize risk, and ensure the safe, reliable, and controllable 
development of AI. 
 
China possesses a favorable foundation for the development of AI. The nation has: 
deployed the National Key Research and Development Plan’s key special projects, such as 
intelligent manufacturing; issued and implemented the “Internet +” and AI Three-Year 
Activities and Implementation Program, releasing a series of measures from science and 
technology research and development; and promoted applications and industrial 
development, and other aspects. As a result of many years of continuous accumulation, 
China has achieved important progress in the field of AI, with the number of international 
scientific and technology papers published and the number of inventions patented ranked 
second in the world, while achieving important breakthroughs in certain domains of core 
crucial technologies. Leading the world in voice recognition and visual recognition 
technologies; initially possessing the capability for leapfrog development in adaptive 
autonomous learning, intuitive sensing, comprehensive reasoning, hybrid intelligence, and 
swarm intelligence, etc.; with Chinese information processing, intelligent monitoring, 
biometric identification, industrial robots, service robots, and unmanned driving gradually 
entering practical application; AI innovation and entrepreneurship have become 
increasingly active, and a number of leading enterprises have accelerated their growth, 


4 
receiving widespread concern and recognition internationally. Accelerate the accumulation 
of technological capabilities and massive data resources, the organization integration of 
both the huge demand for applications and an open market environment, which together 
constitute China’s unique advantage in AI development. 
 
At the same time, we must also clearly see that there is still a gap between China’s overall 
level of development of AI relative to that of developed countries—lacking major original 
results in the basic theory, core algorithms, key equipment, high-end chips, major products 
and systems, foundational materials, components, software and interfaces, etc. Scientific 
research institutions and enterprises do not yet possess international influence upon 
ecological cycles and supply chain, lacking a systematic research and development layout; 
cutting-edge talent for AI is far from meeting demand. Adapting to the development of AI 
requires the urgent improvement of basic infrastructure, policies and regulations, and 
standards systems. 
 
Facing a new situation and new demands, we must take the initiative to pursue and adapt 
to change, firmly seize the major historic opportunity for the development of AI, stick 
closely to development, study and evaluate the general trends, take the initiative to plan, 
grasp the direction, seize the opportunity, lead the world in new trends in the development 
of AI, serve economic and social development, and support national security, promoting 
the overall elevation of the nation’s competitiveness and leapfrog development. 
 
II.  The Overall Requirements 
(1)  Guiding Ideology 
  
Comprehensively implement the spirit of the 18th Party Congress and 18th Central 
Committee’s Third, Fourth, Fifth, and Sixth Plenary Sessions. Thoroughly study and 
implement the spirit of General Secretary Xi Jinping’s series of important sayings and new 
concepts, new ideas, and new strategy for governing the country; according to the “five in 
one” overall layout and “four comprehensives” strategic layout, conscientiously implement 
the CPC Central Committee and State Council decision-making arrangements, deeply 
implement the innovation-driven development strategy to accelerate the deep integration 
of AI with the economy, society and national defense as a primary line, to enhance: 
scientific and technological innovation capacity for a new generation of AI as the main 
direction of attack; intelligent economy development; smart society construction; 
protecting national security; building of knowledge clusters, technology clusters, and 
industry clusters mutually integrated with talent, system, and culture, for a mutually 
supporting ecosystem, advancing intelligentization as the center of humanity’s sustainable 
development. Comprehensively enhance society’s productive forces, comprehensive 
national power, and national competitiveness, in order to provide strong support to 
accelerate the construction of an innovative new-type nation and global science and 
technology power, to achieve the two centennial goals and the great rejuvenation of the 
Chinese nation. 


5 
 
(2)  The Basic Principles 
  
Technology-Led. Grasp the global development trend of AI, highlight the deployment of 
forward-looking research and development, explore the layout in key frontier domains, 
long-term support, and strive to achieve transformational and disruptive breakthroughs in 
theory, methods, tools, and systems; comprehensively enhance original innovation 
capability in AI, accelerate the construction of a first-mover advantage, to achieve high-
end leading development. 
  
Systems Layout. According to the different characteristics of foundational research, 
technological research and development, industrial development, and commercial 
applications, formulate a targeted systems development strategy. Fully give play to the 
advantages of the socialist system to concentrate forces to do major undertakings, 
promote the planning and layout of projects, bases, and a talent pool, organically link 
already-deployed major projects and new missions, continue current urgent needs and 
long-term development echelons, construct innovation capacity, create a collaborative 
force for institutional reforms and the policy environment. 
 
Market-Dominant. Follow the rules of the market, remain oriented toward application, 
highlight companies’ choices on the technological line and primary role in the development 
of commercial product standards, accelerate the commercialization of AI technology and 
results, and create a competitive advantage. Grasp well the division of labor between 
government and the market, better take advantage of the government in planning and 
guidance, policy support, security and guarding, market regulation, environmental 
construction, the formulation of ethical regulations, etc.  
 
Open-Source and Open. Advocate the concept of open-source sharing, and promote the 
concept of industry, academia, research, and production units each innovating and in 
principal pursuing joint innovation and sharing. Follow the coordinated development law for 
economic and national defense construction; promote two-way conversion and application 
for military and civilian scientific and technological achievements and co-construction and 
sharing of military and civilian innovation resources; form an all-element, multi-domain, 
highly efficient new pattern of civil-military integration. Actively participate in global 
research and development and management of AI, and optimize the allocation of 
innovative resources on a global scale. 
(3)  Strategic Objectives 
  
These are divided into the following three steps: 
 
First, by 2020, the overall technology and application of AI will be in step with globally 
advanced levels, the AI industry will have become a new important economic growth point, 
and AI technology applications will have become a new way to improve people’s 
livelihoods, strongly supporting [China’s] entrance into the ranks of innovative nations and 
comprehensively achieving the struggle toward the goal of a moderately prosperous 
society. 


6 
  
● By 2020 China will have achieved important progress in a new generation of AI 
theories and technologies. It will have actualized important progress in big data 
intelligence, cross-medium intelligence, swarm intelligence, hybrid enhanced 
intelligence, and autonomous intelligence systems, and will have achieved 
important progress in other foundational theories and core technologies; the 
country will have achieved iconic advances in AI models and methods, core devices, 
high-end equipment, and foundational software. 
● The AI industry’s competitiveness will have entered the first echelon internationally. 
China will have established initial AI technology standards, service systems, and 
industrial ecological system chains. It will have cultivated a number of the world's 
leading AI backbone enterprises, with the scale of AI’s core industry exceeding 150 
billion RMB, and exceeding 1 trillion RMB as driven by the scale of related industries. 
● The AI development environment will be further optimized, opening up new 
applications in important domains, gathering a number of high-level personnel and 
innovation teams, and initially establishing AI ethical norms, policies, and 
regulations in some areas. 
 
Second, by 2025, China will achieve major breakthroughs in basic theories for AI, such that 
some technologies and applications achieve a world-leading level and AI becomes the 
main driving force for China’s industrial upgrading and economic transformation, while 
intelligent social construction has made positive progress. 
  
● By 2025, a new generation of AI theory and technology system will be initially 
established, as AI with autonomous learning ability achieves breakthroughs in many 
areas to obtain leading research results.  
● The AI industry will enter into the global high-end value chain. This new-generation 
AI will be widely used in intelligent manufacturing, intelligent medicine, intelligent 
city, intelligent agriculture, national defense construction, and other fields, while 
the scale of AI’s core industry will be more than 400 billion RMB, and the scale of 
related industries will exceed 5 trillion RMB.  
● By 2025 China will have seen the initial establishment of AI laws and regulations, 
ethical norms and policy systems, and the formation of AI security assessment and 
control capabilities. 
 
Third, by 2030, China’s AI theories, technologies, and applications should achieve world-
leading levels, making China the world’s primary AI innovation center, achieving visible 
results in intelligent economy and intelligent society applications, and laying an important 
foundation for becoming a leading innovation-style nation and an economic power.   
 
● China will have formed a more mature new-generation AI theory and technology 
system. The country will achieve major breakthroughs in brain-inspired intelligence, 
autonomous intelligence, hybrid intelligence, swarm intelligence, and other areas, 
having important impact in the domain of international AI research and occupying 
the commanding heights of AI technology. 
● AI industry competitiveness will reach the world-leading level. AI should be 
expansively deepened and greatly expanded into production and livelihood, social 
governance, national defense construction, and in all aspects of applications, will 
become an expansive core technology for key systems, support platforms, and the 
intelligent application of a complete industrial chain and high-end industrial 


7 
clusters, with AI core industry scale exceeding 1 trillion RMB, and with the scale of 
related industries exceeding 10 trillion RMB. 
● China will have established a number of world-leading AI technology innovation and 
personnel training centers (or bases), and will have constructed more 
comprehensive AI laws and regulations, and an ethical norms and policy system. 
(4)  Overall Deployment 
  
The development of AI is a complex systemic project related to the overall situation, that 
must be arranged in accordance with “build one system, grasp the two attributes, adhere 
to the trinity, and strengthen the four supports” to form a strategic path for the healthy and 
sustainable development of AI. 
 
Construct an open and cooperative AI technology innovation system. Target the weak 
foundation in original theories, and the key difficulties and deficiencies in major products 
and systems. Establish foundational theories and a common technology system for a new 
generation of AI, laying out the construction of a major scientific and technological 
innovation base. Strengthen the high-end talent team in AI to promote innovation and 
cooperative interactions. Form a continuous innovation capability for AI. 
 
Grasp AI’s characteristic high degree of integration of technological attributes and social 
attributes. It is necessary not only to increase efforts in the research and development and 
applications of AI, maximizing the potential of AI, but also to predict AI’s challenges, 
coordinate industrial policies, innovate in policies and social policies, achieve the 
coordination of encouraging development and reasonable regulation, and maximize risk 
prevention.  
 
Adhere to the promotion of the trinity of breakthroughs in AI research and development, 
product applications, and fostering industry development. Adapt to the characteristics and 
trends of AI development. Strengthen the deep integration of the innovation chain and 
industrial chain, the interactive evolution of technology supply and market demand. Take 
technological breakthroughs to promote domain applications and industrial upgrading. 
Through application demonstrations, promote the optimization of technologies and 
systems. At the same time as greatly promoting technology applications and industrial 
development, strengthen long-term R&D layout and research. Achieve rolling development 
and continuous improvement. Ensure that theory is in the front, the technological 
commanding heights are occupied, and applications are secure and controllable. 
  
Fully support science and technology, the economy, social development, and national 
security.  Drive comprehensive elevation on national innovative capability with AI 
technological breakthroughs. Lead in the process of constructing a global science and 
technology power. Through strengthening intelligent industry and cultivating the intelligent 
economy, create a new growth cycle for China’s next decade or even decades of economic 
prosperity. Through building an intelligent society, promote the improvement of people’s 
livelihoods and welfare and implement people-centric development thinking. Through AI, 
elevate national defense strength and assure and protect national security.  


8 
III.  Focus Tasks 
 
Based on the overall picture of national development, accurately grasp the global 
development trends of AI, find the correct openings for breakthroughs and directions for 
the main thrust, comprehensively strengthen basic science and technology innovation 
capabilities, comprehensively expand the depth and breadth of application in focus areas, 
and comprehensively enhance the built-in intelligence levels of applications in economic 
and social development, as well as in national defence. 
(1)  Build open and coordinated AI science and technology 
innovation systems 
 
Focus on increasing the supply of AI innovation sources; strengthen deployments in areas 
such as advanced basic theory, key general technologies, basic platforms, talent teams, 
etc.; stimulate open-source sharing; systematically enhance sustained innovation 
capabilities; ensure that our country's AI science and technology levels ascend to the 
leading global ranks; and make ever more contributions to the development of global AI. 
1. Establish basic theory systems for a new generation of AI 
 
Focus on major advanced scientific AI questions; concurrently deal with present needs and 
long-term developments; make breakthroughs in basic AI application theory bottlenecks; 
give priority to deploying basic research that may trigger paradigmatic change in AI; 
stimulate the intersection and convergence of disciplines; and provide powerful scientific 
reserves for the sustained development and profound application of AI. 
 
Make breakthroughs in basic application theory bottlenecks. Aim at basic theoretical 
orientations with clear applied objectives, which promise to trigger an upgrade of AI 
technology, strengthen basic theoretical research on big data intelligence, cross-media 
sensing and computing, human-machine blended intelligence, mass intelligence, 
autonomous cooperation and decision-making, etc. Focus on breakthroughs in big data 
intelligence, unsupervised learning, comprehensive deep reasoning and other such difficult 
issues. Establish data-driven cognitive computing models with natural language 
understanding at the core, and shape capabilities to go from big data to knowledge, and 
from knowledge to decision-making. Focus on breakthroughs in cross-media sensing and 
computing theory, including theories and methods for: low-cost and low-energy smart 
sensing, active sensing in complex landscapes, listening comprehension in the natural 
environment as well as language sensing, autonomous multimedia learning, etc. Realize 
superhuman sensing and highly-dynamic, high-dimensional, and multi-model distributed 
large-landscape sensing. The focuses on breakthroughs in blended and enhanced 
intelligence theory are: theories on human-machine cooperative and blended 
environmental understanding, decision-making, and learning; intuitive reasoning and 
causal models, recall and knowledge evolution, etc.; realizing blended and enhanced 
intelligence where learning and reflection approach or exceed human intelligence levels. 
The focuses for breakthroughs in collective intelligence theory are: theories and methods 
for the organization, emergence and learning of collective intelligence; establishment of 
expressible and computable mass intelligence incentive algorithms and models; and 
shaping Internet-based collective intelligence theory systems. The focuses for 


9 
breakthroughs in autonomous coordination, control and optimized decision-making theory 
are: theories concerning coordination sensing and interaction aimed at autonomous 
unmanned systems; autonomous coordination control and optimized decision-making; 
knowledge-driven human-machine-object triangular coordination and interoperation, etc.; 
and shaping novel theoretical systems and frameworks for innovation in autonomous 
intelligence and unmanned systems. 
 
Arrange advanced basic theoretical research. Aim for a direction that may trigger a 
paradigmatic change in AI, far-sightedly arrange research on high-level machine learning, 
brain-inspired intelligence computing, quantum smart computing, and other such cross-
domain basic theories. The focuses for breakthroughs in high-level machine learning 
theory are theories and methods concerning self-adaptive learning, autonomous learning, 
etc., and realizing AI with high interpretative and strong generalization capabilities. The 
focuses for breakthroughs in brain-inspired intelligence computing theory are: theories 
concerning brain-inspired information encoding, processing, recall, learning and reasoning; 
the creation of brain-inspired complex systems and brain-inspired control theories and 
methods; and establishment of new large-scale brain-inspired intelligence computing 
models and brain-inspired understanding computing models. The focuses for 
breakthroughs in quantum computing theory are: methods for quantum-accelerated 
machine learning; establishment of high-performance computing and quantum computing 
convergence models; and shaping high-efficiency, accurate, and autonomous quantum AI 
system setups. 
 
Launch cross-disciplinary exploratory research. Promote the intersection and convergence 
of AI with neurology, cognitive science, quantum science, psychology, mathematics, 
economics, sociology and other such related basic disciplines; strengthen basic theoretical 
mathematical research to guide the development of AI algorithms and models; focus on 
researching the basic theoretical questions of AI legal principles; support exploratory 
research that is strongly original, and where there is no consensus; encourage scientists to 
explore freely; dare to overcome front-line scientific difficulties in AI; create ever more 
original theory; and make ever more original discoveries. 
 
Box 1: Basic Theories 
1. Big data intelligence theory. Research new data-driven and knowledge-driven AI 
methods, theories and methods for sensing computing theory with natural language 
understanding, images and figures at the core, comprehensive deep reasoning and 
creative AI theories and methods, basic theories and frameworks on smart decision-
making with incomplete information, data-driven common AI data models and 
theories, etc. 
2. Cross-media sensing and computing theory. Research sensing that exceeds human 
visual abilities, active visual sensing and computing aimed at the real world, 
auditory sensing and computing of natural acoustic scenes, language sensing and 
computing in an environment of natural interaction, human sensing and computing 
aimed at asynchronous orders, autonomous learning aimed at smart media sensing, 
and urban omnidimensional smart sensing and reasoning engines. 
3. Hybrid and enhanced intelligence theory. Research hybridization and convergence 
where “the human is in the loop,” behavioral strengthening through human-
machine smart symbiosis and brain-machine coordination, intuitive machine 


10 
reasoning and causal models, associative recall models and knowledge evolution 
methods, complex data and task blended and enhanced intelligence learning 
methods, cloud robotics coordination computing methods, and situational 
comprehension and human-machine group coordination in real-world 
environments. 
4. Swarm intelligence theory. Research swarm intelligence structural theory and 
organizational methods, swarm intelligence incentive mechanisms and emergence 
mechanisms, swarm intelligence learning theories and methods, common swarm 
intelligence computing paradigms and models. 
5. Autonomous coordination and control, and optimized decision-making theory. 
Research coordination sensing and interaction aimed at autonomous unmanned 
systems, coordination, control and optimized decision-making aimed at 
autonomous and unmanned systems, knowledge-driven human-machine-object 
triangular coordination and interoperability theories. 
6. High-level machine learning theory. Research basic statistical learning theories, 
reasoning and decision-making under uncertainty, distributed learning and 
interaction, learning while protecting privacy, small-sample learning, deep intensive 
learning, unsupervised learning, semi-supervised learning, active learning and other 
such learning theories and efficient models. 
7. Brain-inspired intelligence computing theory. Research theories and methods on 
brain-inspired sensing, brain-inspired learning, and brain-inspired recall 
mechanisms and computing blends, brain-inspired complex systems, brain-inspired 
control, etc. 
8. Quantum intelligent computing theory. Explore cognitive quantum models and 
intrinsic mechanisms, research efficient quantum intelligence models and 
algorithms, high-performance and high-bitrate quantum AI processors, real-time 
quantum AI systems that can exchange information with the outside world, etc. 
 
2.  Build a next-generation AI key general technology system 
 
Focusing on the urgent need to raise China's international competitiveness in AI, next-
generation AI key general technology R&D and deployment should make algorithms the 
core; data and hardware the foundation; and upping capabilities in sensing and 
recognition, knowledge computing, cognitive reasoning, executing motion, and human-
machine interface the emphasis; in order to form openly compatible, stable and mature 
technological systems. 
 
Knowledge computing engine and knowledge service technology. Key breakthroughs in 
knowledge processing, deep search, and visual interactive core technology; realization of 
automatic acquisition of incrementally growing knowledge; possession of concept 
discernment, object discovery, attribute prediction, evolutionary knowledge modeling, and 
relationship discovery capabilities; the formation of multi-billion-scale, multi-source, 
multi-disciplinary, multi-data type, and cross-medium knowledge maps. 
 
Cross-medium analytical reasoning technology. Key breakthroughs in cross-medium 
unified indicators; relational understanding and knowledge mining; knowledge map 
structure and learning; knowledge evolution and reasoning; intelligent description and 


11 
generation, etc., technology. Realization of cross-medium knowledge indicators, analysis, 
mining, reasoning, evolution, and utilization. Construct analytic reasoning engines. 
 
Key swarm intelligence technology. Key breakthroughs on the basis of the popularization 
of the internet, mass collaboration, knowledge resource management, and open sharing, 
etc., technologies. Building frameworks to display swarm intelligence knowledge. Realize 
the integration and strengthening of swarm intelligence-based knowledge acquisition and 
swarm intelligence under open development conditions. Support swarm perception, 
cooperation, and evolution at a national, tens-of-millions scale.  
 
New architecture and new technology for hybrid and enhanced intelligence. Key 
breakthroughs in human-machine interaction for perception and execution integration 
models, new types of intelligent computing-fronted sensors, common use hybrid 
architecture, etc., core technologies. Build autonomous, environmentally adaptable hybrid 
enhanced intelligent systems, human-machine hybrid enhanced intelligent systems and 
support environments. 
 
Intelligent technologies of autonomous unmanned systems. Key breakthroughs in 
autonomous unmanned system computing architecture, complex situational environment 
perception and understanding, real-time accurate positioning, adaptable, intelligent 
navigation in complex environments, etc., general technologies. Unmanned and 
autonomously controlled systems including automobiles, ships, automatic driving in 
traffic, etc., intelligent technologies. Develop service robots, special-purpose robots, etc., 
core technologies and support unmanned system application and manufacturing 
development.  
 
Intelligent virtual reality modeling technology. Key breakthroughs in intelligent modeling 
technology for virtual counterparts. Increasing the sociality, diversity, and lifelike quality of 
virtual reality intelligent counterpart behavior. Realize the organic integration, high 
efficiency, and interactivity of virtual reality and augmented reality, etc., technologies. 
 
Intelligent computing chips and systems. Key breakthroughs in high energy 
efficiency, reconfigurable brain-inspired computing chips and brain-inspired visual sensor 
systems with computational imaging capabilities. Research and develop high-efficiency 
brain-inspired neural network architectures and hardware systems with autonomous 
learning capabilities. Realize brain-inspired intelligent systems with multimedia sensory 
information understanding, intelligence growth, and common sense reasoning capabilities. 
 
Natural language processing technology. Key breakthroughs in natural language grammar 
logic, word-concept symbols, and deep semantic analysis core technologies. Advance 
effective human-machine communication and free interaction. Realize multi-style, multi-
language, multi-domain natural language intelligent understanding and automated 
[results] generation. 
 
 
 


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Box 2: Key General Technologies 
 
1. Knowledge computing engines and knowledge service technology. Researching 
knowledge computing and visual interaction engines; researching innovative 
design, digital creation, and commercial intelligence with visual media at the core; 
developing large-scale organic data knowledge discovery. 
2. Cross-medium analytic reasoning technology. Researching cross-medium unified 
indicators, connected understanding and knowledge mining, knowledge map 
building and learning, knowledge evolution and inference, intelligent description 
and generation, etc., technology; developing cross-medium analytic reasoning 
engine and verification systems. 
3. Key swarm intelligence technology. Developing swarm intelligence's active 
perception and discovery, knowledge gain and generation, cooperation and sharing, 
evaluation and evolution, human-machine integration and enhancement, self-
preservation and mutual security, etc., key technology studies; building service 
system architecture for the crowd intelligence space; researching mobile crowd 
intelligent coordinated decision making and control technologies. 
4. Hybrid enhanced intelligent new architectures and technologies. Researching hybrid 
enhanced intelligent core technology and cognitive computing frameworks; new-
model hybrid computing architectures, human-machine collective driving, online 
intelligent learning technology, and hybrid enhanced frameworks for simultaneous 
management and control. 
5. Autonomous unmanned systems intelligent technology. Researching unmanned 
autonomous control intelligent technology for automobiles, ships, traffic, automatic 
driving, etc.; service, space, maritime, and polar robot technology; unmanned 
workshop/intelligent factory intelligent technology; high-end intelligent control 
technology and autonomous unmanned operating systems. Researching 
positioning, navigation, recognition, etc., robotic and mechanical arm autonomous 
control technology for visual sensing in complex environments.  
6. Virtual reality intelligent modeling technology. Researching mathematical 
expression and modeling methods for virtual counterpart intelligent behavior; 
problems such as natural, persistent, and deep exchange between users and virtual 
counterparts and virtual environments; intelligent counterpart modeling technology 
and method systems. 
7. Intelligent computing chips and systems. Researching neural network processors, 
as well as high-energy efficiency, reconfigurable brain-inspired computing chips, 
etc.; new-model perception chips and systems, intelligent computing system 
structure and systems, and AI operating systems. Researching architectures 
suitable for AI hybrid architectures, etc. 
8. Natural language processing technology. Researching short text computing and 
analysis technology, cross-language text mining technology and turning toward 
semantic comprehension technology for machine cognitive intelligence, and 
human-machine interaction systems for multimedia information comprehension. 
 
 
 
 
 
 


13 
3. Coordinate the layout of AI innovation platforms 
  
Construct AI innovation platforms. Strengthen the foundational support for AI research and 
development and applications. AI open-source hardware and software infrastructure 
platforms should focus on building and supporting unified computing frameworks for 
knowledge reasoning, probability statistics, depth learning, and other AI paradigms. Form 
and promote an ecological chain of platforms for interaction and synergies among AI 
software, hardware, and intelligent clouds. The group intelligent service platform should 
focus on the construction of knowledge resource management and the open sharing tools 
based on the large-scale cooperation on the Internet. Create a platform and service 
environment for the innovation of the industry and university. The hybrid enhanced 
intelligent support platforms should focus on the construction of a heterogeneous real-
time computing engine supporting large-scale training and a new computing clusters, 
providing a service-oriented, systematic platform and solution for complex intelligent 
computing. Autonomous unmanned system support platform focuses on the construction 
of autonomous system environmental awareness, autonomous collaborative control, 
intelligent decision-making and other AI common core technology support systems. Create 
development and test environments for open, modular, reconfigurable autonomous 
unmanned systems. AI basic data and security detection platforms should focus on the 
construction of AI for the public data resource library, the standard test data set, cloud 
service platform, the formation of AI algorithms and platform security test evaluation 
methods, techniques, norms and tools, promoting the open sourcing and openness of all 
kinds of common software and technology platform. Promote military-civilian sharing and 
joint use for all kinds of platforms in accordance with the requirements of deep military-
civil integration related provisions. 
 
Box 3: Basic Support Platforms 
 
1. AI Open-Source Hardware and Software Infrastructure and Platforms. Establish big 
data and AI open-source software platforms, terminal, and cloud collaborative AI 
cloud service platforms, new multi-intelligent sensor and integrated platforms, new 
product design platforms based on AI hardware, and future network, big data 
intelligent service platforms. 
2. Group Intelligent Service Platforms. Establish group knowledge-based computing 
and support platforms, science and technology public service systems, group 
intelligent software development and verification automation systems, group 
intelligent software learning and innovation systems, open environment cluster 
decision-making systems, and group-sharing economic service systems. 
3. Hybrid Enhanced Intelligent Support Platforms. Establish AI supercomputing 
centers, large-scale super intelligent computing support environments, online 
intelligent education platforms, “human-in-the-loop” driving brains, intelligent 
platforms for complexity analyses and risk assessment in industrial development, 
intelligent security platforms to support nuclear power security operations, and 
research and development and testing platforms for human-machine joint driving 
technology. 
4. Autonomous Unmanned System Support Platforms. Establish common core 
technology and support platforms, independent unmanned systems, independent 
control of unmanned aerial vehicles, and automatic driving support platforms for 
auto, ship and rail traffic, service robots, space robots, marine robots, polar robot 


14 
support platforms, technical support platforms for intelligent factory and intelligent 
control equipment, etc. 
5. AI Basic Data and Security Detection Platforms. Construct artificial data-oriented 
public data resource libraries, standard test data sets, and cloud service platforms. 
Establish test models and evaluation models for the security of AI algorithms and 
platforms. Research and develop security evaluation tools for AI algorithms and 
platforms. 
4. Accelerate the training and gathering of high-end AI talent 
Make the construction of a high-end talent team of the utmost importance in the 
development of AI. Adhere to the combination of training and introduction. Improve the AI 
education system, strengthen the construction of a talent pool and echelons, especially 
accelerate the introduction of the world’s top talent and young talent, forming China’s AI 
top talent base. 
Cultivate high-level of AI innovative talents and teams. Support and cultivate the 
development potential of leading AI talent. Strengthen professional and technical 
personnel training for basic research, applied research, operations and maintenance 
aspects of AI. Pay attention to the training of compound talents, focusing on cultivating 
vertical composite talents for AI theory, methods, technology, products, and application, 
and compound talents who master the “AI +” economy, society, management, standards, 
law, and other horizontal areas. Through major research and development tasks and base 
and platform construction, converge high-end talents in AI. Create high-level innovation 
teams in a number of AI key domains. Encourage and guide domestic innovative talents 
and the teams to strengthen cooperation with the world’s top AI research institutions. 
 
Increase the introduction of high-end AI talent. Open up specialized channels and 
implement special policies to achieve the precise introduction of peak AI talent. Focus on 
the introduction of international top scientists and high-level innovation teams in neural 
awareness, machine learning, automatic driving, intelligent robots, and other areas. 
Encourage the use of flexible introduction of AI talent through project cooperation, 
technical advice, etc. Coordinate the use of the “Thousands Talents” plan and other 
existing talent plans to strengthen the field of AI talents, especially through the 
introduction of outstanding young talent. Improve enterprise human capital cost 
accounting and related policies. Encourage enterprises and scientific research institutions 
to introduce AI talent. 
 
Construct an AI academic discipline. Improve the disciplinary layout of the AI domain. 
Establish AI majors. Promote the construction of a discipline in the domain of AI. Establish 
AI institutes as soon as possible in pilot institutions. Increase the enrollment places for 
masters and PhDs in working in AI and related disciplines. Encourage colleges and 
universities to broaden the content of AI professional education on an original basis. Create 
a new model of “AI + X” compound professional training, attaching importance to cross-
integration of professional education for AI and mathematics, computer science, physics, 
biology, psychology, sociology, law, and other disciplines. Strengthen cooperation in 
production and research. Encourage universities, research institutes, enterprises and other 
institutions to carry out the construction of an AI discipline. 
 


15 
(2) Fostering a high-end, highly efficient smart economy 
 
Accelerate the fostering of an AI industry with a major leading and driving effect, stimulate 
the profound convergence of AI and all industrial areas, and create data-driven smart 
economic patterns with human-machine coordination, cross-sectoral convergence, and 
joint creation and sharing. Data and knowledge will become the first factor for economic 
growth; human-machine coordination will become the mainstream method of production 
and service; cross-sectoral convergence will become an important economic model; joint 
creation and sharing will become basic characteristics of the economic ecology; 
individualized demands and made-to-order will become new consumption trends; and 
productivity will increase substantially, drive industries to migrate towards the high end of 
value chains, powerfully support the development of the real economy, and 
comprehensively increase the quality and efficiency of economic development. 
 
1. Forcefully develop new AI industries 
 
Accelerate the transformation and application of key AI technologies, stimulate the 
integration of technologies with commercial model innovation, promote the innovation of 
smart products in focus areas, vigorously foster new AI business models, compose high-
end industry chains, and forge AI industry groups with international competitiveness. 
 
Smart software and hardware. Develop operating systems, databases, intermediary 
devices, development tools, and other such key software and hardware aimed at AI; make 
breakthroughs in graphic processing and other such core hardware; research solution 
plans for smart systems in pattern recognition, voice understanding, machine translation, 
smart interaction, knowledge processing, control and decision-making, etc.; and foster 
and expand basic software and hardware industries aimed at AI. 
 
Smart robots. Tackle core components and special sensors for smart robots, perfect 
hardware interface standards, software interface standards, and safe usage standards for 
smart robots. Research and develop smart industrial robots and smart service robots, 
realize large-scale application, and enter into global markets. Research, produce, and 
popularize space robots, maritime robots, polar robots, and other such special kinds of 
smart robots. Establish smart robot standard systems and security norms. 
 
Smart delivery tools. Develop self-driving vehicles and rail traffic systems; strengthen the 
integration and coordination of vehicle load sensing, automatic driving, the Internet of cars, 
the Internet of Things, and other such technologies; develop smart traffic sensing systems, 
create national indigenous automatic driving platform technology systems and industrial 
assembly capabilities; and explore self-driving vehicle sharing models. Develop consumer 
and commercial unmanned aircraft and unmanned ships, and establish and trial 
specialized service systems for authentication, monitoring, technology competition, etc., 
perfect management measures for the space and maritime areas. 
 
Virtual reality and augmented reality. Make breakthroughs in key technologies such as 
high-performance software modelling, content capturing and generation, augmented 
reality and human-machine interaction, integrated environments and tools, etc. Research 
and create virtual display devices, optical devices, high-performance three-dimensional 
display devices, development engines, and other such products. Establish standards and 


16 
evaluation systems for virtual reality and augmented reality technologies, products, and 
services, and promote their converged application in focus sectors. 
 
Smart terminals. Accelerate the research and development of smart terminal core 
technologies and products, develop new-generation smart phones, on-board smart 
terminals for cars, and other such mobile smart terminal products and equipment. 
Encourage the research and development of smart watches, smart earpieces, smart 
glasses, and other such wearable terminal products, and expand product forms and 
application services. 
 
Basic Internet of Things devices. Develop high-sensitivity and highly reliable smart sensors 
and chips supporting the new-generation Internet of Things. Make progress in core Internet 
of Things technologies such as RFID and short-distance machine communications, as well 
as key components such as low-power processors. 
 
2. Accelerate and promote the upgrade of industrial intelligentization 
 
Promote the converged innovation of AI in all sectors. Launch AI application 
demonstrations and trials in focus sectors and areas such as manufacturing, agriculture, 
logistics, finance, commerce, household goods, etc. Promote the application of AI at scale, 
and comprehensively upgrade the smartness level of industrial development. 
 
Smart manufacturing. Focus on the major demands for building a strong manufacturing 
country, move forward the integrated application of systems such as key technologies and 
equipment for smart manufacturing, core supporting software, the industrial internet, etc. 
Research and develop smart products and smart connected products, tools and systems 
that can be used in smart manufacturing, and smart manufacturing cloud service 
platforms. Popularize smart manufacturing processes, distributed smart manufacturing, 
networked coordinated manufacturing, long-distance diagnosis and operational services, 
and other such novel manufacturing models. Establish smart manufacturing standard 
systems, and move forward with the intelligentization of manufacturing activities across 
the entire lifecycle. 
 
Smart agriculture. Research and formulate smart agricultural sensing and control systems, 
smart agricultural equipment, autonomous tasking systems for farming equipment across 
fields, etc. Establish and complete smart agriculture information remote sensing and 
monitoring networks integrating air, space, and land components. Establish model 
agriculture big data smart decision-making and analysis systems, launch trials of smart 
farms, smart plant factories, smart pastures, smart fisheries, smart orchards, smart farm 
produce processing workshops, green and smart farm product supply chains and other 
such integrated applications. 
 
Smart logistics. Strengthen research, development and broad use of smart logistics 
equipment for smart loading, unloading, and transportation; parcel sorting, processing and 
delivery; etc. Establish smart deep-sensing storage systems, and enhance storage and 
operational management levels and efficiency. Perfect smart logistics public information 
platforms and command systems, product quality authentication and tracing systems, 
smart distribution and dispatch systems, etc. 
 


17 
Smart finance. Establish big data systems for finance, and enhance multimedia data 
processing and comprehension capabilities for finance. Innovate smart financial products 
and services, develop new financial business models. Encourage the financial sector to use 
smart customer service, smart inspection, and other such technologies and equipment. 
Build smart warning and prevention systems for financial risk. 
 
Smart commerce. Encourage the application of cross-media analysis and reasoning, 
knowledge computing engines and knowledge services, and other such new technologies 
in the commercial area, and popularize AI-based novel commercial services and decision-
making systems. Build cross-medium data platforms covering geographic positioning, 
online media, urban basic data, etc., and support enterprises' launching smart services. 
Encourage the provision of made-to-order commercial smart decision-making services 
focusing on individual demands and enterprise management. 
 
Smart household goods. Strengthen the converged application of AI technology and 
household and building systems, and enhance the smartness levels of building facilities 
and household goods. Research, develop, and use household connection and interactivity 
agreements, as well as interface standards suited for different application settings. 
Enhance sensing and connection capabilities of household electrical appliances, durable 
goods and other such household products. Support smart household enterprises in 
innovating new service models, and promote interactive and sharing solutions and plans. 
 
3. Forcefully develop smart enterprises 
 
Promote the upgrading of enterprises' smartness levels on a large scale. Support and guide 
enterprises to use new AI technologies in core operational segments such as design, 
production, management, logistics, sales, etc. Build novel enterprise organization 
structures and operational models; create smart and converged business models for 
manufacturing, services, and finance; and develop individualized made-to-order; and 
broaden smart product supply. Encourage large-scale Internet enterprises to build cloud 
manufacturing platforms and service platforms, and provide online key industry software 
and model databases aimed at manufacturing enterprises. Launch outsourcing services for 
manufacturing capacity, and promote the development of smartness among small and 
mid-size enterprises. 
 
Popularize the use of smart factories. Strengthen the application and demonstration of key 
technologies and system methods for smart factories. Focus on popularizing production 
line reconstruction and dynamic smart control, production faculty smart interconnection 
and cloud data collection, multi-dimensional human-machine-object coordination, 
interoperability, and other such technologies. Encourage and guide enterprises to build 
factory big data systems, networked distributed production facilities, etc. Realize the 
networking of production equipment, the visualization of production data, the transparency 
of production processes, and the automation of production sites; and enhance the 
smartness levels of factory operational management. 
 
Accelerate the fostering of AI industry-leading enterprises. Accelerate the creation of 
global leading AI enterprises and brands in advantageous areas such as unmanned aircraft, 
speech recognition, pattern recognition, etc. Accelerate the fostering of a batch of key 
enterprises in novel areas such as smart robots, smart cars, wearable equipment, virtual 
reality, etc. Support AI enterprises to strengthen their patent structures, and take the lead 


18 
in or participate in the formulation of international standards. Promote domestic 
advantageous enterprises, sectoral organizations, scientific research bodies, higher 
education institutes, etc., to jointly establish the AI Industry and Technology Innovation 
Alliance of China. Support key backbone enterprises to build open source hardware 
factories, open source software platforms, create innovative ecologies integrating all kinds 
of resources, stimulate small and mid-size AI enterprises to develop and to be used in all 
areas. Support all kinds of bodies and platforms to provide specialized services aimed at AI 
enterprises. 
4. Create AI innovation heights 
Combined with each locality’s foundation and advantages, according to the field of AI 
applications classifications, advance the layout of the relevant industries. Encourage local 
industry chains and innovation chains around AI. Gather high-end factors, high-end 
enterprises, and high-end talent. Build AI industry clusters and heights of innovation. 
 
Launch AI innovation application pilot demonstrations. In areas where the AI foundation is 
favorable and its development potential bigger, organize and launch national AI innovation 
experiments. Explore systems and mechanisms, policy and regulation, the cultivation of 
talent, and other major reforms. Promote the transformation of the AI achievements, major 
product integrated innovation, and demonstration of applications. Form replicable, 
promotable experience, leading to the promotion of intelligent economy and intelligent 
social development. 
 
Construct national AI industrial parks. Rely upon national independent innovation 
demonstration areas and the national high-tech industry development zone and other 
innovative vectors. Strengthen science and technology talent, finance, policy, and other 
elements of the optimal allocation and combination. Accelerate the construction of AI 
industry innovation cluster. 
 
Construct national AI mass innovation bases. Relying on colleges and universities and 
scientific research institutes concentrated in localities, build AI field professionalized 
innovation platforms and other new entrepreneurial service agencies. Construct a number 
of low-cost, convenient, all-factor, open-style AI ‘hackerspaces.’ Improve incubation 
services system, promote the transformation of AI scientific and technological 
achievements, and support AI innovation and entrepreneurship. 
(3) Construct a safe and convenient intelligent society 
 
Based on the goal of improving people's living standards and quality, speed up and deepen 
the applications of AI, increase the level of intelligentization of the whole society to form an 
all-encompassing and ubiquitous intelligent environment. Increasingly, repetitive, 
dangerous tasks will be completed by AI, while individual creativity will play a greater role. 
Form more high-quality and high comfort jobs; make precision intelligent services more 
diverse, such that people can maximize their enjoyment of high quality services and 
convenient life. Through a substantial increase in the level of intelligentization of social 
governance, make social operations more safe and efficient. 


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1. Develop convenient and efficient intelligent services 
 
Accelerate the application of innovative AI throughout education, health care, pension and 
other urgent needs involving people's livelihood, to provide for the public personalized, 
diversified, high-quality services. 
 
Intelligent Education. Utilize intelligent technology to accelerate and promote a personnel 
training model and reform to teaching methods; establish new-type education systems, 
including intelligent learning and interactive learning. Launch the construction of 
intelligent campuses; promote AI in teaching, management, resource construction, and 
other full-scale applications. Develop three-dimensional integrated teaching field, based 
on big data intelligent online learning and education platforms. Develop intelligent 
educational assistants; establish intelligent, fast and comprehensive education analysis 
system. Establish a learner-centered educational environment, and provide precision-
deployed education services, achieve daily education and lifelong education. 
 
Intelligent Medical Care. Promote the use of new models and new methods of AI treatment, 
establish a rapid, accurate intelligent medical system. Explore intelligent hospital 
construction, develop human-machine coordinated surgical robots and intelligent clinic 
assistants. Pursue research and development on flexible wearable, biologically compatible 
physiological monitoring systems, research and development of human-computer 
collaboration intelligent clinical diagnosis and treatment programs. Achieve intelligent 
image recognition, pathology classification, and intelligent multi-disciplinary consultation. 
Carry out large-scale genome recognition, proteomics, metabolomics, and other research 
and development of new drugs based on AI, promote intelligent pharmaceutical regulation. 
Strengthen epidemic intelligence monitoring, prevention, and control. 
 
Intelligent Health and Elder Care Systems. Strengthen community intelligent health 
management, achieve breakthroughs in big data analysis, Internet of Things, and other key 
technologies. Research and develop health management wearable equipment and home 
intelligent health testing and monitoring equipment. Promote changes in health 
management from point-like monitoring to continuous monitoring, from short process 
management to long process management. Construct intelligent elder care communities 
and institutions; build a safe and convenient intelligent pension infrastructure system. 
Strengthen the intelligentization of products for elderly persons and intelligent products 
suitable for the aged. Develop audio-visual aid equipment, physical auxiliary equipment, 
and other intelligent home care equipment, expanding the elderly’s activity space. Develop 
mobile social and service platform for the elderly and emotional escort assistant to 
enhance the quality of life of the elderly. 
2. Promote the intelligentization of social governance 
 
Promote the application of AI technology for administrative management, judicial 
management, urban management, environmental protection, and other hot and difficult 
issues in social governance, to promote the modernization of social governance. 
 
Intelligent Government. Develop an AI platform for government services and decision-
making. Develop a decision-making engine for the open environment. Promote 
applications in research on complex social problems, policy assessment, risk warning, 


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emergency response, and other major matters of strategic decision-making. Strengthen 
the integration of government information resources and accurate forecasting of public 
demands, and smooth communication channels between the government and the public. 
 
Smart Courts. Construct a set of trial, personnel, data applications, judicial disclosure, and 
dynamic monitoring into an integrated court data platform. Promote AI applications for 
applications including evidence collection, case analysis, and legal document reading and 
analysis. Achieve the intelligentization of courts and trial systems and trial capacity.  
 
Smart Cities. Build an intelligentized city infrastructure, develop intelligent buildings, and 
promote the intelligentization, transformation, and upgrading of underground corridors and 
other municipal infrastructure. Construct urban big data platforms to build a 
heterogeneous, integrated data system for urban operations and management. Achieve 
comprehensive perception and deep understanding of the operation of complex urban 
systems for urban infrastructure and urban green space, wetlands, and other important 
ecological elements. Research and develop to build community public service information 
systems. Promote community service system and residents’ intelligent home system 
collaboration. Promote the intelligentization of the full lifecycle of urban planning, 
construction, and management. 
 
Smart Transportation. Research, establish, and operate vehicle automatic driving and road 
coordination technology systems. Research and develop information and integrated data 
platforms for transportation under complex multi-dimensional conditions. Establish 
intelligentized transportation command, control, and integrated operations. Actualize 
intelligent transportation obstacle removal and integrated management and coordination 
and command. Build intelligent transportation monitoring, management, and service 
systems covering the ground, tracks, low altitude, and the sea. 
 
Intelligent Environmental Protection. Establish an intelligent monitoring large data 
platforms and systems covering the atmosphere, water, soil, and other environmental 
areas. Build information-sharing and intelligent environmental monitoring networks and 
service platforms for coordination of land and sea, integration of atmosphere and earth, 
and upwards and downwards synergies. Research and develop intelligent forecasting 
models and method and early warning programs for energy resource consumption and 
environmental pollutant discharge. Strengthen the Beijing-Tianjin-Hebei, Yangtze River 
Economic Zone, and other major national strategic regions’ construction of intelligent 
prevention and control system for environmental protection and sudden environmental 
events. 
3. Use AI to enhance public safety and security capabilities 
Advance the deepening of AI applications in the field of public safety. Promote the 
construction of public safety and intelligent monitoring and early warning and control 
systems. Research and develop a variety of detection sensor technology, video image 
information analysis and identification technology, biometric identification technology, 
intelligent security and police products. Establish intelligent monitoring platform for 
comprehensive community management, new criminal investigations, anti-terrorism, and 
other urgent needs. Strengthen the upgrading and intelligentization of security equipment 
for key public areas. Support carrying out public security regional demonstrations based on 
AI according to the conditions of the community or the city. Strengthen the use of AI for 
food safety protection, food classification, warning level, food safety risks and assessment, 


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and the establishment of intelligent food safety early warning system. Strengthen the 
effective monitoring of natural disasters, natural disasters, around the earthquake disaster, 
geological disasters, meteorological disasters, floods and disasters and marine disasters 
and other major natural disasters, to build an intelligent monitoring and early warning and 
comprehensive response platform. 
4. Promote social interaction and mutual trust 
Give full play to the role of AI technology in enhancing social interaction and promoting 
credible communication. Strengthen the next generation of social network research and 
development, accelerate innovation in augmented reality, virtual reality, and other 
technologies to promote the integrative use of virtual environments and physical 
environments to meet personal perception, analysis, judgment and decision-making real-
time information needs, and to achieve the smooth transition of different scenes of work, 
study, life, and entertainment. In order to improve the interpersonal communication needs, 
develop intelligent assistant products with the ability to accurately understand the needs 
of emotional interaction. Promote the integration of blockchain technology and AI, 
establish a new social credit system, and minimize the cost and risks of interpersonal 
communication. 
(4) Strengthen military-civilian integration in the AI domain 
  
Deepen implementation of military-civilian integration development strategy, to promote 
the formation of an all-element, multi-field, high efficiency AI military-civilian integration 
pattern. Build new generation AI based on research and development in the common 
theory and critical common technology. Establish mechanisms to normalize 
communication and coordination among scientific research institutes, universities, 
enterprises and military industry units. Promote military-civilian two-way transformation of 
AI technology. Strengthen a new generation of AI technology as a strong support to 
command and decision-making, military deduction, defense equipment, and other 
applications. Guide defense domain AI technology toward civilian applications. Encourage 
and advantage people’s scientific research forces to participate in the domain of national 
defense for major scientific and technological innovation tasks in AI. Promote all kinds of AI 
technology to become quickly embedded in the field of national defense innovation. 
Strengthen the construction of military and civilian AI technology standard systems.  
Promote the overall layout and open sharing of science and technology innovation 
platforms and bases. 
(5) Build a safe and efficient intelligent infrastructure system 
 
Vigorously promote the construction of intelligent information infrastructure. Enhance the 
traditional level of intelligent infrastructure to form a smart economy, intelligent society 
and national defense needs of the infrastructure system. Speed up the promotion of 
information transmission as the core of the digital, network information infrastructure. 
Take integration awareness, transmission, storage, computing, and processing in 
intelligent information infrastructure changes. Optimize network infrastructure, research 
and develop the layout of fifth generation mobile communication (5G) systems. Improve the 
Internet of Things infrastructure. Accelerate the integration of information network 
construction. Improve low-latency, high-throughput transmission capacity. Coordinate the 


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use of big data infrastructure, strengthen data security and privacy protection, to provide 
massive data support for AI research and development and extensive applications. Build 
high-performance computing infrastructure, and enhance the service support capabilities 
of supercomputing centers for AI applications. Construct distributed and efficient energy 
Internet, form multi-energy support complementary, timely, and effective access to new 
energy networks. Promote intelligent energy storage facilities, intelligent electricity 
facilities, energy supply and demand information to achieve real-time matching and 
intelligent response. 
  
  
Box 4: Intelligentized Infrastructure 
 
1.   Network Infrastructure. Speed up the layout of real-time collaborative AI 5G 
enhanced technology research and the development and application of space-
oriented collaborative AI for the construction of high-precision navigation and 
positioning networks to strengthen the core of intelligent sensing technology 
research and key facilities. Develop intelligent industrial support, driving networks, 
etc., to study the intelligent network security architecture. Speed up the 
construction of integrated information network for space and earth, promoting a 
space-based information network, the future of the Internet, mobile communication 
network of the full integration. 
2. 
   Big Data Infrastructure. Rely on a national data sharing exchange platform, open 
data platform and other public infrastructure. Construct governance, public 
services, industrial development, technology research and development, and other 
fields of big data information databases Support the implementation of national 
governance data applications. Integrate various types of social data platforms and 
data center resources. Create nationwide integrated service capabilities with 
reasonable layout and linkages. 
3. High-performance computing infrastructure. Continue to strengthen the 
supercomputing infrastructure, distributed computing infrastructure and cloud 
computing center construction. Build sustainable development of high-
performance computing application for the ecological environment. Promote the 
next generation of supercomputer research and development and applications. 
(6) Plan a new generation of AI major science and technology 
projects 
 
For the development of China’s AI needs and weak links, establish of a new generation of AI 
major scientific and technological projects. Strengthen the overall co-ordination, clear the 
boundaries of the tasks and the focus of research and development. Form a new 
generation of AI major scientific and technological projects as the core, and use existing 
R&D layout to support the “1 + N” AI program. 
 
“1” refers to a new generation of AI scientific and technological mega-projects, focusing on 
forward-looking layout for basic theories and key common technologies, including the 
study of big data intelligence, cross-media perception and computing, hybrid enhanced 
intelligence, group intelligence, autonomous collaborative control, and decision-making 
theory. Research knowledge computing engines and knowledge service technologies, 
cross-medium analysis reasoning technology, key swarm intelligence technologies, new 


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architecture and new technology for hybrid enhanced intelligent, autonomous unmanned 
control technology, and basic theory and common technology for open-source shared AI. 
Continue to carry out the development of AI prediction and research, strengthening the 
economic and social impact of and countermeasures for AI. 
 
“N” refers to the national planning and deployment of AI research and development 
projects. Focusing on strengthening the new generation of AI with the convergence major 
scientific and technological projects, collaborative impetus for research, technological 
breakthroughs and product development applications. Strengthen the convergence of 
major national science and technology projects. Support AI hardware and software 
development in the “Hegaoji” Megaproject,1 integrated circuit equipment and other national 
science and technology major projects. Strengthen mutual support for AI and other 
“Technological Innovation 2030 - Mega-Projects.” Accelerate the use of AI to provide 
support for major technical breakthroughs in brain science and brain computing, quantum 
information and quantum computing, intelligent manufacturing and robotics, and big data 
research. The National Key Research and Development Plan will continue to promote high-
performance computing and other key special applications, while increasing support for AI-
related technology research and development and application; the National Natural 
Science Foundation will strengthen cross-disciplinary research and support for free 
exploration in the field of AI. Focus on special deployment and strengthen the application 
of AI technology demonstrations to the deep sea space station, health protection, and 
other major projects, smart cities, intelligent agricultural equipment and other Key National 
R&D Projects. Support the openness and sharing of research results on basic theory of AI 
and common technology through other basic science and technology plans. 
 
Innovate in the organization and implementation of models for new generation AI major 
scientific and technological projects. Adhere to focus on doing things, focusing on the 
principle of breakthrough. Give full play to the role of market mechanisms to mobilize 
departments, local, business and social forces to promote the implementation of all 
aspects. Pursue clear management responsibility, regular assessments, to strengthen the 
dynamic adjustments and improve management efficiency. 
IV.  Resource Allocation 
 
Fully use existing finances, bases and other such stored resources, comprehensively plan 
the allocation of international and domestic innovation resources, give rein to the guiding 
role of finance administration input and policy incentives, and the dominant role of the 
market in allocating resources, impel enterprises and society to expand input, and create a 
new pattern of multi-sided support through finance administration funding, financial 
capital, and social capital. 
                                               
 
1 Translator’s note: This refers to the Medium and Long-term Plan for S&T Development 
2006-2020 megaproject: core (he) electronic devices, high-end (gao) general-purpose 
chips, and basic (ji) software. 


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(1) Establish financial support mechanisms guided by the 
financial administration and dominated by the market 
 
Comprehensively plan multiple-channel financial input by government and markets, 
strengthen support through finance administration funding, enliven existing resources, and 
provide support for fundamental and advanced AI research, critical public technology 
breakthroughs, result transformation, base and platform construction, innovative 
application demonstrations, etc. Use existing policy input funds to support AI programs to 
meet conditions, encourage leading and backbone enterprises and industrial innovation 
alliances to take the lead in establishing marketized AI development bases. Use angel 
investment, risk investment, start-up investment funds, financial market funding and many 
other such channels to guide social capital to support AI development. Vigorously use 
governmental and social capital cooperation and other such models and guide social 
capital to participate in the implementation of major AI programmes and the 
transformation and application of scientific and technological achievements.  
(2) Optimize arrangements to build AI innovation bases 
According to the national-level science and technology innovation base arrangements and 
frameworks, comprehensively promote a few internationally advanced innovation bases in 
the area of AI construction. Guide existing AI-related national focus laboratories, corporate 
national focus laboratories, national engineering laboratories, and other such bases, and 
conduct research focused on an advanced direction of a new generation of AI. According to 
regulatory procedure, build technological and industrial innovation bases related to the AI 
area with enterprises in the lead, and in cooperation between industry, scholarship, and 
research. Give rein to the driving role of leading and backbone enterprises concerning 
technological innovation demonstrations. Develop specialized public maker spaces in the 
AI area, stimulate the precise linkage of the newest technological achievements, resources 
and services. Fully give rein to the role of all kinds of innovation bases in concentrating 
talent, finance, and other such innovation resources; make breakthroughs in basic and 
advanced AI theory and key common technologies; and launch application 
demonstrations. 
(3) Comprehensively plan international and domestic innovation 
resources 
  
Support domestic AI enterprises to cooperate with international leading AI schools, 
scientific research institutes and teams. Encourage domestic AI enterprises to "go out," 
and provide conveniences and services to powerful AI enterprises conducting foreign 
mergers or acquisitions, share investment, start-up investment, establishing foreign 
research centres, etc. Encourage foreign AI enterprises and research institutes to establish 
research and development centers in China. With the support of the “One Belt, One Road” 
strategy, promote the construction of international AI science and technology cooperation 
bases, joint research centres, etc.; accelerate the broad application of AI technologies in 
countries along the “One Belt, One Road.” Promote the establishment of international AI 
organizations, jointly formulate related international standards. Support related sectoral 
associations, alliances, and service bodies to build globalized service platforms aimed at AI 
enterprises. 


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V.  Guarantee Measures 
Aiming at the realistic requirements of promoting the healthy and rapid development of AI 
in China, it is necessary to deal with the possible challenges of AI, form an institutional 
arrangement to adapt to the development of AI, build an open and inclusive international 
environment, and reinforce the social foundation of AI development. 
(1) Develop laws, regulations, and ethical norms that promote the 
development of AI 
 
Strengthen research on legal, ethical, and social issues related to AI, and establish laws, 
regulations and ethical frameworks to ensure the healthy development of AI. Conduct 
research on legal issues such as civil and criminal responsibility confirmation, proteciton of 
privacy and property, and information security utilization related to AI applications. 
Establish a traceability and accountability system, and clarify the main body of AI and 
related rights, obligations, and responsibilities. Focus on autonomous driving, service 
robots, and other application subsectors with a comparatively good usage foundation, and 
speed up the study and development of relevant safety management laws and regulations, 
to lay a legal foundation for the rapid application of new technology. Launch research on AI 
behavior science and ethics and other issues, establish an ethical and moral multi-level 
judgment structure and human-computer collaboration ethical framework. Develop an 
ethical code of conduct and R&D design for AI products, strengthen the assessment of the 
potential hazards and benefits of AI, and build solutions for emergencies in complex AI 
scenarios. China will actively participate in global governance of AI, strengthen the study of 
major international common problems such as robot alienation and safety supervision, 
deepen international cooperation on AI laws and regulations, international rules and so on, 
and jointly cope with global challenges. 
(2) Improve key policies for the support of AI development 
 
Implement tax incentives for small and mid-sized enterprise and startup AI development, 
and, using high-tech enterprises, tax incentives, R&D cost deductions, and other policies, 
support the development of AI enterprises. Improve the implementation of open data and 
protection-related policies, launch open public data reform pilots to support the public and 
enterprises in fully tapping the commercial value of public data, and promote the 
application of AI innovation. China will study the policy system of education, medical care, 
insurance, and social assistance to adapt to AI, and effectively deal with the social 
problems brought by AI. 
(3) Establish an AI technology standards and intellectual property 
system 
 
Conduct research on strengthening the AI standards framework system.  Adhere to the 
principles of security, availability, interoperability, and traceability; and gradually establish 
and improve the basic basis of AI, interoperability, industry applications, network security, 
privacy protection, and other technical standards. Speed up the promotion of autonomous 
driving, service robot, and other application sector industry associations in developing 
relevant standards. Encourage AI enterprises to participate in or lead the development of 


26 
international standards, and a technical standards "going out" approach to promote AI 
products and services in overseas applications. Strengthen the protection of intellectual 
property in the field of AI, improve the field of AI technology innovation, patent protection, 
and standardization of interactive support mechanisms to promote the innovation of AI 
intellectual property rights. Establish AI public patent pools to promote the use of AI and 
the spread of new technologies. 
(4) Establish an AI security supervision and evaluation system 
 
Strengthen research and evaluation of the influence of AI on national security and secrecy 
protection; improve the security protection system of human, technology, material, and 
management support; and construct an early warning mechanism of AI security 
monitoring. Strengthen the development of AI technology prediction, research and follow-
up research, adhere to a problem-oriented, accurate grasping of technology and industry 
trends. Enhance the awareness of risk, pay attention to risk assessment and prevention 
and control, and strengthen prospective prevention and restraint guidance. In the near 
term focus on the impact on employment, with a long-term focus on the impact on social 
ethics, to ensure that the development of AI falls with the sphere of secure and 
controllable. Establish and improve an open and transparent AI supervision system, the 
implementation of design accountability, and application of the supervision of a two-tiered 
regulatory structure, to achieve management of the whole process of AI algorithm design, 
product development and results application. Promote AI industry and enterprise self-
discipline, and earnestly strengthen management, increase disciplinary efforts aimed at 
the abuse of data, violations of personal privacy, and actions contrary to moral ethics. 
Strengthen AI cybersecurity technology research and development, strengthen AI products 
and systems cybersecurity protection. Develop dynamic AI research and development 
evaluation mechanisms, focus on AI design, product and system complexity, risk, 
uncertainty, interpretability, potential economic impact, and other issues. Develop a 
systematic testing methods and indicators system. Construct a cross-domain AI test 
platform to promote AI security certification, and assessment of AI products and systems 
key performance. 
(5) Vigorously strengthen the training of an AI labor force 
 
Accelerate the study of the employment structure brought on by AI, changes in 
employment methods, and the skills demand of new occupations and jobs, establish a 
lifelong learning and employment training system to meet the needs of the intelligent 
economy and intelligent society, and support institutions of higher learning, vocational 
schools and socialization training Institutions to carry out AI skills training.  Substantially 
increase the professional skills of workers to meet the development requirements of 
China's AI to bring high-quality jobs. Encourage enterprises and organizations to provide AI 
skills training for employees. Strengthen the re-employment training and guidance of 
workers to ensure the smooth transfer of simple and repetitive workers due to AI. 
(6) Carry out a wide range of AI scientific activities 
  
Support the development of a variety of AI scientific activities, encourage the broad masses 
of scientific and technological workers to join the promotion of AI popular science, and 


27 
comprehensively improve the level of the whole society on the application of AI. Implement 
a universal intelligence education project. In the primary and secondary schools, set up AI-
related courses, and gradually promote programming education to encourage social forces 
to participate in the promotion and development of educational programming software and 
games. Construct and improve the AI science infrastructure, give full play to all kinds of AI 
innovation base platforms and other popular science roles, encourage AI enterprises, and 
research institutions to build open source platforms for public open AI research and 
development, plus production facilities or exhibition halls. Support the development of AI 
competitions, encourage the formation of a variety of AI science creational work efforts. 
Encourage scientists to participate in AI science. 
VI.  Organization and Implementation 
 
The development plan for a new generation of AI is a far-sighted scheme affecting the 
overall picture and the long term. We must strengthen organizational leadership, complete 
mechanisms, take aim at objectives, keep tasks closely in view, realistically grasp 
implementation with a spirit of hammering nails, and carry out the blueprint to the end. 
(1) Organizational leadership 
 
According to the unified deployment of the Party Center and the State Council, the National 
Science and Technology Structural Reform and Innovation System Construction Leading 
Small Group will take the lead in comprehensive planning and coordination, it will 
deliberate major tasks, major policies, major questions, and major work arrangements. 
Promote AI-related legal and regulatory construction. Guide, coordinate and supervise 
relevant departments in carrying out the deployment and implementation of tasks from the 
plan. With the support of the interministerial joint conferences for national science and 
technology planning (earmarks, funding, etc.) management, the Ministry of Science and 
Technology will, together with relevant departments, be responsible for moving forward the 
implementation of major science and technology programmes for a new generation of AI, 
and strengthen linkages and coordination with other programmatic tasks. Establish an AI 
Plan Implementation Office. This office will be part of the Ministry of Science and 
Technology and will be concretely responsible for moving the implementation of the plan 
forward. Establish an AI Strategy Advisory Committee, to research major far-sighted and 
strategic questions concerning AI and to provide advice and assessment concerning major 
policy decisions on AI. Move forward with the construction of an AI think tank, support all 
kinds of think tanks to launch research on major AI questions, and provide strong and 
powerful support for the development of AI. 
(2) Guarantee implementation 
 
Strengthen the deconstruction of plan tasks, clarify responsible work units, schedules and 
arrangements, formulate annual and phase-type implementation plans. Establish 
monitoring and evaluation mechanisms for the implementation situation of the plan, such 
as annual assessment and intermediate evaluation. Adapt to the characteristics of the 
rapid development of AI, and strengthen dynamic adjustment of plans and programs on the 


28 
basis of the progress of tasks, the completion of intermediate objectives, new trends in 
technological development, etc. 
(3) Trials and demonstrations 
 
We must formulate concrete plans for major AI tasks and focus policy measures, and 
launch trials and demonstrations. Strengthen comprehensive guidance over trials and 
demonstrations in all departments and all localities, quickly summarize and disseminate 
replicable experiences and methods. Advance the healthy and orderly development of AI 
through advance trials and guiding demonstrations. 
(4) Public opinion guidance 
 
Fully use all kinds of traditional media and new media to quickly propagate new progress 
and new achievements in AI, to let the healthy development of AI become a consensus in 
all of society, and muster the vigor of all of society to participate in and support the 
development of AI. Conduct timely public opinion guidance, and respond even better to 
social, theoretical, and legal challenges that may be brought about by the development of 
AI. 
  
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