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CHAPTER 7:

AI Policy andNational Strategies

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Overview

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Working Group15

Chapter Highlights

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Summits and Meetings

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Bilateral Agreements

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7.1 NATIONAL AND REGIONALAI STRATEGIES

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7.3 U.S. PUBLIC INVESTMENT IN AI

Published Strategies

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Federal Budget for Non-Defense AI R&D 17

2017

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2018

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U.S. Department of Defense
Budget Request

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2019

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U.S. Government Contract Spending

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2020

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Total Contract Spending

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Contract Spending by
Department and Agency

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7.4 AI AND POLICYMAKING

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Legislation Records on AI

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Strategies in Development(as of December 2020)
Strategies in Public Consultation

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Strategies Announced

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Highlight: National AI Strategies
and Human Rights

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U.S. Congressional Record

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7.2 INTERNATIONALCOLLABORATION ON AI

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Mentions of AI and ML in
Congressional/Parliamentary
Proceedings

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ACCESS THE PUBLIC DATACHAPTER 7 PREVIEW

Central Banks

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U.S. AI Policy Papers

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APPENDIX

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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 thetechnology 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 andthen reviews activities on the intergovernmental level. The chapter then takes acloser look at public investment in AI in the United States as well as how legislative
bodies, central banks, and nongovernmental organizations are responding to thegrowing need to institute a policy framework for AI technologies.

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CHAPTERHIGHLIGHTS

CHAPTER HIGHLIGHTS• Since Canada published the world’s first national AI strategy in 2017, more than 30 othercountries 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 thedevelopment of AI for all.
• In the United States, the 116th Congress was the most AI-focused congressional session inhistory. The number of mentions of AI by this Congress in legislation, committee reports, andCongressional Research Service (CRS) reports is more than triple that of the 115th Congress.

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7.1 N AT I O N A L
AND REGIONAL
A I S T R AT E G I E SThis 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. “AIstrategy” 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.

7.1 NATIONAL AND REGIONALAI STRATEGIESTo guide and foster the development of AI, countries and regions around the world are establishing strategies andinitiatives 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.

COUNTRIESWITH PUBLISHEDAI STRATEGIES: 32

COUNTRIESDEVELOPING
AI STRATEGIES: 22

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Published Strategies
2017

Canada• AI Strategy: Pan Canadian AI Strategy• Responsible Organization: Canadian Institute forAdvanced 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 mostcomprehensive in the world. It encompasses areas
including R&D and talent development througheducation and skills acquisition, as well as ethical norms
and implications for national security. It sets specifictargets, including bringing the AI industry in line withcompetitors by 2020; becoming the global leader in fields
such as unmanned aerial vehicles (UAVs), voice andimage 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 2019and released the “Beijing AI Principles” in May 2019 withCHAPTER 7 PREVIEWa 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 ofAI development. The first phase focuses on the utilizationof data and AI in related service industries, the secondon 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.

OthersFinland: Finland’s Age of Artificial Intelligence
United Arab Emirates: UAE Strategy for Artificial
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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 thecommitments and actions agreed on by EU member
states, Norway, and Switzerland to increase investment
and build their AI talent pipeline. It emphasizes the valueof 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 andat least EUR 4.9 billion (USD 5.4 billion) for other aspectsof 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 inresearch 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

CHAPTER 7 PREVIEW• Recent Updates: The French National Research Institutefor Digital Sciences (Inria) has committed to playing acentral role in coordinating the national AI strategy andwill 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 emphasison the public interest and working to better the lives ofpeople 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 to2025• Recent Updates: In November 2019, the government
published an interim progress report on the Germany AI
strategy.

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2018 (continued)

India• AI Strategy: National Strategy on
Artificial Intelligence: #AIforAll• Responsible Organization: National Institution forTransforming India (NITI Ayog)• Highlights: The Indian strategy focuses on both
economic growth and ways to leverage AI to increasesocial inclusion, while also promoting research toaddress important issues such as ethics, bias, andprivacy 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 andInformation Technology released its own proposal toset 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 governanceframework, mapping the needs of AI in various industries,
and identifying governmental best practices with anemphasis 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.

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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 thegovernment and identifies five foundations for a
successful industrial strategy: becoming the world’s mostinnovative economy, creating jobs and better earnings
potential, infrastructure upgrades, favorable businessconditions, 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, witha major emphasis on AI technologies that promise to
revolutionize warfare.

OthersSweden: National Approach to Artificial Intelligence
Taiwan: Taiwan AI Action Plan

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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 steeringcommittee that will oversee the implementation andmonitoring 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 strongemphasis on its national interests and lays down
guidelines for the development of an “informationsociety” between 2017 and 2030. These include anational 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

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the use of AI, developing practical measures to introduce
AI algorithms, providing neural network developers withcompetitive 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, agovernment 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, healthcare, education, and safety and security.
• Funding (December 2020 conversion rate): While the2019 strategy does not mention funding, in 2017 thegovernment 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 themeasures 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 ensuretechnical standards for the safe development, testing,and deployment of AI technologies. The White Housealso emphasizes developing an AI-ready workforce andsignals a commitment to collaborating with foreign
partners while promoting U.S. leadership in AI. The
initiative, however, lacks specifics on the program’stimeline, whether additional research will be dedicatedto 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 applicationsin the private sector, including principles that encourage
AI innovation and growth and increase public trust andconfidence 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 tofacilitate the use of AI by businesses and to streamline
regulations to create a more favorable environment forthe 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.

CHAPTER 7 PREVIEW• Funding (December 2020 conversion rate):
KRW 2.2 trillion (USD 2 billion)• Recent Updates: N/A

OthersColombia: 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 andInnovation Agency (BRIN), Agency for the Assessment andApplication 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, healthservices, 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 criticalsectors: 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 thatwill attract at least 300 AI and data startups.
• Funding: N/A• Recent Updates: During the summit where theSaudi government released its strategy, the country’s
National Center for Artificial Intelligence (NCAI) signed
collaboration agreements with China’s Huawei andAlibaba Cloud to design AI-related Arabic-language
systems.

CHAPTER 7 PREVIEWOthersHungary: 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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AND REGIONAL
A I S T R AT E G I E SStrategies 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 theeconomy, ethics, development, education, and jobs, andto 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 incollaboration with IBM, the University of São Paulo, andthe São Paulo Research Foundation.

OthersCyprus: National Strategy for Artificial Intelligence
Ireland: National Irish Strategy on Artificial Intelligence
Poland: Artificial Intelligence Development Policy in
PolandUruguay: Artificial Intelligence Strategy for Digital

Government

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 developinga 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

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AND REGIONAL
A I S T R AT E G I E SStrategies 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 hugebenefits for the agricultural sector.

Australia• Related Documents: Artificial Intelligence Roadmap /An AI Action Plan for all Australians• Responsible Organizations: Commonwealth Scientificand 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 scienceagency, 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 andmachine learning (ML). In addition, CSIRO published aresearch paper on Australia’s AI Ethics Framework in 2019and 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 ofIndustry and Technology; Scientific and TechnologicalResearch Council of Turkey; Science, Technology andInnovation Policies Council• Status: The strategy has been announced but not yet
published. According to media sources, it will focus

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on talent development, scientific research, ethics andinclusion, and digital infrastructure.

OthersAustria: 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 (newsarticle)Switzerland: Artificial Intelligence (official guidelines)
Tunisia: National Artificial Intelligence Strategy (taskforce announced)
Ukraine: Concept of Artificial Intelligence Development
in Ukraine AI (concept document)
Vietnam: Artificial Intelligence Development Strategy(official announcement)

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AND REGIONAL
A I S T R AT E G I E S

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

National AI Strategies and Human Rights
In 2020, Global Partners Digital and Stanford’s
Global Digital Policy Incubator published areport examining governments’ national AI
strategies from a human rights perspective,
titled “National Artificial Intelligence Strategies
and Human Rights: A Review.” The reportassesses the extent to which governments
and regional organizations have incorporated
human rights considerations into their national
AI strategies and made recommendations topolicymakers looking to develop or review AI
strategies in the future.
The report found that among the 30 states andtwo regional strategies (from the European
Union and the Nordic-Baltic states), a numberof strategies refer to the impact of AI on humanrights, with the right to privacy as the mostcommonly 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 humanrights 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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Table 7.1.1: Mapping human rights
referenced in national AI strategies
HUMAN RIGHTS
MENTIONEDSTATES/REGIONALORGANIZATIONS

The right to privacy

Australia, Belgium, China,
Czech Republic, Germany,
India, Italy, Luxembourg, Malta,
Netherlands, Norway, Portugal,
Qatar, South Korea, UnitedStates

The right
to equality/
nondiscrimination

Australia, Belgium, Czech
Republic, Denmark, Estonia, EU,
France, Germany, Italy, Malta,
Netherlands, Norway

The right to aneffective remedy

Australia (responsibility
and ability to hold humans
responsible), Denmark, Malta,
Netherlands

The rights tofreedom of thought,
expression,and access to
information

France, Netherlands,
RussiaThe right to work

France, Russia

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7. 2 I N T E R N AT I O N A L
C O L L A B O R AT I O N
ON AI

7.2 INTERNATIONALCOLLABORATION ON AI
Given the scale of the opportunities and the challenges
presented by AI, a number of international efforts haverecently been announced that aim to develop multilateral
AI strategies. This section provides an overview of thoseinternational initiatives from governments committed toworking together to support the development of AI for all.
These multilateral initiatives on AI suggest thatorganizations are taking a variety of approaches totackle the practical applications of AI and scale thosesolutions for maximum global impact. Many countries
turn to international organizations for global AI norm
formulation, while others engage in partnerships orbilateral 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 particularfocus 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 andtechnology bilateral agreements that stress cooperation
on AI as part of the Digital Silk Road under the Beltand Road (BRI) initiative framework. For example, AI is
mentioned in China’s economic cooperation under the BRI
Initiative with the United Arab Emirates.

I N T E R G OV E R N M E N TA LI N I T I AT I V E SIntergovernmental working groups consist of experts andpolicymakers from member states who study and reporton the most urgent challenges related to developing anddeploying 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.

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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 theEuropean Union (as of December 2020)• Host of Secretariat: OECD
• Focus Areas: Responsible AI; data governance; the futureof work; innovation and commercialization• Recent Activities: Two International Centres ofExpertise—the International Centre of Expertise inMontreal for the Advancement of Artificial Intelligenceand 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•R ecent Activities: Since its launch at the recommendation

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Artificial IntelligenceIndex Report 2021of the EU AI strategy in 2018, HLEG presented the EU Ethics
Guidelines for Trustworthy Artificial Intelligence and aseries of policy and investment recommendations, aswell as an assessment checklist related to the guidelines.
Ad Hoc Expert Group (AHEG) for the Recommendationon the Ethics of Artificial Intelligence• Participants: United Nations Educational, Scientific andCultural Organization (UNESCO) member states• Host: UNESCO•F
 ocus 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 Statesof 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 ofAI 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

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7. 2 I N T E R N AT I O N A L
C O L L A B O R AT I O N
ON AI

B I L AT E R A L AG R E E M E N T S
Bilateral agreements focusing on AI are another formof international collaboration that has been gaining in
popularity in recent years. AI is usually included in the
broader context of collaborating on the development ofdigital economies, though India stands apart for investingin developing multiple bilateral agreements specifically
geared toward AI.
India and United Arab Emirates
Invest India and the UAE Ministry of Artificial Intelligencesigned 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 inpartnership 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 theuse 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 RelationshipEconomic Working Group, that the two countries willenter into a bilateral dialogue on advancing AI in line withshared democratic values and further cooperation in AI
R&D efforts.
India and Japan
India and Japan were said to have finalized an agreementin October 2020 that focuses on collaborating on digitaltechnologies, including 5G and AI.
French and Germany
France and Germany signed a road map for a FrancoGerman Research and Innovation Network on artificial
intelligence as part of the Declaration of Toulousein October 2019 to advance European efforts in thedevelopment and application of AI, taking into accountethical guidelines.

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7. 3 U.S. P U B L I C
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.

7.3 U.S. PUBLIC INVESTMENT IN AI
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, thefirst 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 partof 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.

Federal civilian agencies—thoseagencies that are not part of the
DOD or the intelligence sector—
allocated USD 973.5 million to
AI R&D for FY 2020, a figurethat rose to USD 1.1 billion once
congressional appropriations
and transfers were factored in.
U.S. FEDERAL BUDGET for NON-DEFENSE AI R&D, FY 2020-21
Source: U.S. NITRD Program, 2020 | Chart: 2021 AI Index Report

1,500

Budget (in Millions of U.S. Dollars)

FEDERAL BUDGET FOR
N O N - D E F E N S E A I R& D

1,000

500

0
2020 (Request)

2020 (Enacted)

2021 (Request)
Figure 7.3.1

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U.S. D E PA R T M E N T O F D E F E N S E A I
R& D B U D G E T R E Q U E S T

7. 3 U.S. P U B L I C
INVESTMENT
IN AI

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.

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 thatsheds light on its spending on AI R&D.
With 305 unclassified DOD R&D programs specifying the useof 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 arefactored in, the true level of funding available to DOD AI R&D
programs in FY 2021 may rise substantially.

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

Budget (in Millions of U.S. Dollars)

5,000

4,000

3,000

DOD Reported
Budget on AI R&D

2,000

DOD Reported
Budget on AI R&D

927

1,000

841

0
2018 (Enacted)

2019 (Enacted)

2020 (Enacted)

2021 (Request)
Figure 7.3.2

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 aradically smaller number: USD 841 million. This relates to the issue of defining where an AI system ends and anothersystem begins; for instance, an initiative that uses AI for drones may also count hardware-related expenditures for thedrones within its “AI” budget request, though the AI software component will be much smaller.

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U.S. G OV E R N M E N T A I - R E L AT E D
C O N T R ACT S P E N D I N G
Another indicator of public investment in AI technologies isthe level of spending on government contracts across thefederal 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 aset 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 andservices has reached an all-time high and shows no sign ofslowing down. However, note that during the procurement
process, vendors may add a bunch of keywords into theirapplications, so some of these things may have a relatively
small AI component relative to other parts of technology.

7. 3 U.S. P U B L I C
INVESTMENT
IN AI

USD 1.5 billion agencies spent in FY 2019 (Figure 7.3.3).
AI spending in 2020 was more than six times higher thanwhat 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

Total Contract Spending

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 ofHomeland Security (USD 112.3 million). DOD, NASA, andthe Department of Health and Human Services top thelist 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 morethan what was spent by the other 44 departments andagencies combined (USD 2.9 billion) over the same period.

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

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, isU.S. GOVERNMENT TOTAL CONTRACT SPENDING on AI, FY 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report

Contract Spending (in Millions of U.S. Dollars)

2,000

1,837

1,500

1,000

500

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

0

Figure 7.3.3

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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

7. 3 U.S. P U B L I C
INVESTMENT
IN AI

Warfighter program, and another to Deloitte Consulting fora four-year, USD 106 million enterprise cloud environment
for the JAIC, known as the Joint Common Foundation.

TOP 10 CONTRACT SPENDING on AI by U.S. GOVERNMENT DEPARTMENT and AGENCY, 2020
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report

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)
0

200

400

600

800

1,000

1,200

Contract Spending (in Millions of U.S. Dollars)

1,400
Figure 7.3.4

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

Department of Defense (DOD)
National Aeronautics andSpace Administration (NASA)
Department of Health andHuman 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)
0

500

1000

1500

2000

2500

3000

Contract Spending (in Millions of U.S. Dollars)

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3500

4000
Figure 7.3.5

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7.4 A I A N D
P O L I CY M A K I N G

7.4 AI AND POLICYMAKINGAs AI gains attention and importance, policies andinitiatives related to the technology are becoming higher
priorities for governments, private companies, technicalorganizations, 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.

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 forthe 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 UnitedStates, 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. andCanada, each count indicates that AI or ML was mentioned
in a particular comment or remark during the proceedings.1

L E G I S L AT I O N R E C O R D S O N A I
The number of congressional and parliamentary
records on AI is an indicator of governmental interestin developing AI capabilities—and legislating issues
pertaining to AI. In this section, we use data from
Bloomberg and McKinsey & Company to ascertain the

MENTIONS of AI in U.S. CONGRESSIONAL RECORD by LEGISLATIVE SESSION, 2001-20
Source: Bloomberg Government, 2020 | Chart: 2021 AI Index Report486

500
Congressional Research Service Reports
Committee Reports

Number of Mentions

400

243

Legislation
300

70

200
149
66

100

173
39

0

4

7

22

17

15

10

8

16

44

107Th
108Th
109Th
110Th
111Th
112Th
113Th
114Th
115Th
116th
(2001-2002) (2003-2004) (2005-2006) (2007-2008) (2009-2010) (2011-2012) (2013-2014) (2015-2016) (2017-2018) (2019-2020)
Figure 7.4.1

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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P O L I CY M A K I N G

U.S. Congressional Record
subcommittees commissioned 70 reports on AI, while
the CRS, tasked as a fact-finding body for members ofCongress, published 243 about AI or referencing AI.

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 triplethat of the 115th Congress. Congressional interest in AI
has continued to accelerate in 2020. Figure 7.4.1 showsthat 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 andMentions of AI and ML in Congressional/
Parliamentary Proceedings
As shown in Figures 7.4.2–7.4.5, the number of mentionsof artificial intelligence and machine learning in theproceedings of the U.S. Congress and the U.K. parliamentcontinued to rise in 2020, while there were fewer
mentions in the parliamentary proceedings of Canada.

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

140

129
120

120

28

Number of Mentions

28

Machine Learning

100

92

Artificial Intelligence

25

80

60
92

101

40
27

67

20
9

8

0

0

7

1

1

1

6

2011

2012

2013

2014

2015

2016

23

2017

2018

2019

2020
Figure 7.4.2

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P O L I CY M A K I N G

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

300

283
37

250

Number of Mentions

Machine Learning
200

192

Artificial Intelligence

183

34

150

138
246

100

179

158

138

51

50

0

0

2011

42

7

4

1

5

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 7.4.3

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 Report40

38

Machine Learning

3

Artificial Intelligence

34

Number of Mentions

30

21

20

18
35

33

21

10

17

2

0

0

0

0

2011

2012

2013

2014

0

0

2015

2016

2017

2018

2019

2020
Figure 7.4.4

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P O L I CY M A K I N G

C E N T R A L BA N KS
Central banks play a key role in conducting currency andmonetary 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.

Figure 7.4.5 shows a significant increase in the mentionof 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 focusedon responses to the economic downturn. Moreover,the Federal Reserve in the United States, Norges Bankin Norway, and the European Central Bank top thelist for the most aggregated number of AI mentions incommunications in the past five years (Figure 7.4.6).

Prattle, a leading provider of automated investment
research solutions, monitors mentions of AI in thecommunications of central banks, including meeting
minutes, monetary policy papers, press releases,
speeches, and other official publications.

MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD, 2011-20
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report

1,000

Number of Mentions

800

600

400

200

225

0
2011

2012

2013

2014

2015

2016

2017

2018

2019

2020
Figure 7.4.5

2 See Science & Technology Review and Scientific American for more details.

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P O L I CY M A K I N G

MENTIONS of AI in CENTRAL BANK COMMUNICATIONS around THE WORLD by BANK, 2016-20 (SUM)
Source: Prattle/LiquidNet, 2020 | Chart: 2021 AI Index Report

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
0

200

400

600

800

1,000

1,200

Number of Mentions

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1,400

1,600

1,800 2,000
Figure 7.4.6

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U.S. A I P O L I CY PA P E R S
What are the AI policy initiatives outside national andintergovernmental governments? We monitored 42
prominent organizations that deliver policy papers on
topics related to AI and assessed the primary topic aswell as the secondary topic on policy papers publishedin 2019 and 2020. (See the Appendix for a complete listof organizations included.) Those organizations areeither U.S.-based or have a sizable presence in the UnitedStates, and we grouped them into three categories: thinktanks, policy institutes and academia (27); civil society
organizations, associations and consortiums (9); andindustry 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

7.4 A I A N D
P O L I CY M A K I N G

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 andinternational 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 andinclusion; 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 havereceived the least attention in U.S. AI policy papers.

U.S. AI POLICY PRODUCTS by TOPIC, 2019-20 (SUM)
Source: Stanford HAI & AI Index, 2020 | Chart: 2021 AI Index ReportInnovation & Technology
Int'l Affairs & Int'l Security
Industry & Regulation
Workforce & Labor
Government & Public Administration
Privacy, Safety & Security
EthicsJustice & Law Enforcement
Equity & Inclusion
Education & Skills
Social & Behavioral Sciences
Health & Biological Sciences
Communications & Media
Secondary Topic

Democracy

Primary Topic

Humanities
Energy & Environment
Physical Sciences
0

20

40

60

80

100

120

140

160

Number of Policy Products
Figure 7.4.7

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APPENDIXC H A P T E R 7:
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APPENDIXB LO O M B E R G G OV E R N M E N T
Bloomberg Government (BGOV) is a subscriptionbased market intelligence service designed to make
U.S. government budget and contracting data moreaccessible 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 fromthree 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 amodel of government spending on artificial intelligencerelated contracts in the fiscal years 2000 through 2021.
BGOV’s model used a combination of governmentdefined 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 AIspecific 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

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congressional documents, including bills, amendments,
bill summaries, Congressional Budget Officeassessments, reports published by congressional
committees, Congressional Research Service (CRS), andothers. 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 AIspecific keywords. Results are organized by a two-year
congressional session.

LIQUIDNET
Prepared by Jeffrey Banner and Steven Nichols

SourceLiquidnet 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 bycentral banks: In the first case, China uses a geopoliticalenvironment simulation and prediction platformthat works by crunching huge amounts of data andthen 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 andlaunched 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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SourceData 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. Thismeans that within an event or conversation, if a member
mentions AI or ML multiple times within their remarks, itwill appear only once. However if, during the same event,
the speaker mentions AI or ML in separate comments (withother 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 ofLords, Westminster Hall, and Committees)
Data was collected using the Find References feature of the
Hansard website of the U.K. Parliament. MGI searched forthe terms “Artificial Intelligence” and “Machine Learning”(quotes included) and catalogued the results. Data is asof 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 aparticular 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

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N AT I O N A L S T R AT E G I E SIntelligence or Machine Learning are separate, as they
were conducted in separate searches. Mentions of theabbreviations AI or ML are not included.

United States (Senate and House ofRepresentatives)
Data was collected using the advanced search featureof 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, andproceedings 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 thereading 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 aresult. 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. A I P O L I CY PA P E R

SourceData collection and analysis was performed by Stanford
Institute of Human-Centered Artificial Intelligence and AI Index.

Organizations
To develop a more nuanced understanding of thethought leadership that motivates AI policy, we tracked
policy papers published by 36 organizations across threebroad categories including:Think Tanks, Policy Institutes & Academia: This includes
organizations where experts (often from academia andthe 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, Brookings28Artificial IntelligenceIndex Report 2021

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 Institutefor 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 Electricaland 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, earthscience• 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

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N AT I O N A L S T R AT E G I E S

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 andimprovements 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 ofwork• 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, humanvalues, human rights, sustainability, explainability,interpretability, decision-making norms

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