2023 Global Artificial Intelligence Infrastructures Reportc

J.P. Singh & Amarda Shehu & Caroline Wesson & Manipriya Dua & David Bray

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This report uses computer science techniques to analyze national and sub-national AI policies published by 54 different countries. It’s the most comprehensive analysis to date on national AI policies. It provides important comparisons and contrasts of national and global priorities for the development and deployment of AI. The report analyzes the empirical determinants of dominant strategies for developing AI around the globe and shows where countries are converging and diverging in their approaches to AI. The cross-national and global comparisons are important for a host of important players in AI including policy-makers, governments, businesses, and civil society organizations.

Executive Summary

In 2016, the United States published its National Artificial Intelligence Research and Development Strategic Plan, usually understood in policy communities as the first statement of its AI infrastructure strategy (Select Committee on Artificial Intelligence, 2016). Since then over 60 countries have announced their national or sectoral AI policies.

This report employs computer science techniques to analyze the published national AI plans of 54 countries. In other words, we employ AI to analyze AI strategies. The report includes an analysis of 213 documents on AI strategies. Apart from national plans, the set includes reports and publications from various government departments, ministries, nation commissions, bodies appointed to forward recommendations for specific issues and sectors.

Our computer science methodology, specifically Latent Dirichlet Analysis (LDA) (Blei, Ng and Jordan, 2003), is calibrated to recognize embedded or latent topics that each document contains. It does so through providing probabilities of words that are most likely to occur together in each document. All documents are analyzed together for a pre-specified number of topics, ascertained through rigorous methodological criteria. The choice of the number of topics reflects fulfillment of various methodological LDA criteria for model stability (consistency) and topic stability (coherence). A document may feature a dominant topic, or a document may contain two or more topics. Further, we employ a technique known ensemble-LDA (e-LDA) to provide stable results assessed over multiple model specifications.

Collectively we present the most detailed and comprehensive empirical analysis undertaken of national AI infrastructures to date. This analysis provides comparisons and contrasts across 54 national strategies and a granular look at what these strategies contain. We note the priorities that are contained in documents, but our analysis also points out the policy depth for particular countries. Policy depth refers to the extent to which countries have covered the entire gamut of issues that comprise an infrastructure, and the institutional and financial resources they have committed to these issues. For example, AI policies from leading powers such as United States and China contain depth for basic research capabilities in science and mathematics, while the European Union policies contain the most depth for data governance and ethics. For example, one of the strategic objectives stated in the Chinese AI strategy states: “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” (State Council, 2017).

We make three major claims:

• There is no grand strategy or conclusion that applies to all AI infrastructures. Countries and clusters of countries feature different objectives and how to achieve them.
• Countries are pursuing a variable mix of similar elements in their national strategies. We propose and utilize the concept of ‘AI Wardrobes’ to show the various elements available for putting together an AI infrastructure and the variable ways in which countries are putting together these wardrobes.
• Clusters of countries pursuing similar strategies are identifiable. Our machine learning algorithms are able to point out some obvious clusters from the European Union, Latin America, and East Asia. But there are also surprises. United Kingdom leads a British influence cluster. Spain is prominent in the Latin American cluster.

Our three major claim are made at three different levels:

• We analyze 54 plans that are taken to be national. These are often ‘performative’. They are as much about national priorities as they are declarations meant for the international community. But they reveal the broad trajectory and differences among national strategies.
• We analyze 213 documents including the national plans that national governments, commissions and departments have published on their AI infrastructures. Unlike, the performativity and differences among national plans, the intra-national plan reveal fewer national differences but a few countries have more policy depth than others. We notice countries that are at the early stages of policies regarding their AI infrastructures, versus those that have detailed regulatory and sub-sector policies.
• We also analyze the 213 documents, regardless of country labels, and here we see the broad topics that stand out in country plans. These include transportation, education, data ethics, and regulation. Looking at the documents we can then understand the countries that dominate these topics and also some broad differences among them.

Based on our analysis we present three policy recommendations:

• Comparative analyses like ours provide countries sign posts and guidelines for their ambitions. There is no one size fits all for designing national AI infrastructures. Different countries have different capabilities and priorities.
• Regulating AI will depend on country preparedness and political systems. Grand pronouncements such as fears about sacrificing our human rights or privacy to machine-led systems in our media about AI need a reality check. Several countries, generally with democratic systems, are putting together or struggling to put together systems of accountability, while others barely feature any such concerns. This provides room to think about governance issues, rather than ceding this authority to machines (or corporations) prematurely.
• AI policies have many good stories to tell about service provision. These include AI applications for health, education and research, and transportation.