The quest for national AI success across the globe: a cluster analysis of national AI strategies

We thank Brookings for this research blog.

Writers: James S. Denford, , and 

The quest for national AI success across the globe   

In a series of papers, the authors have explored comparative differences in the design, deployment, and governance of artificial intelligence (AI) technologies among countries. Our previous papers have explored how different countries viewed their adoption of AI, their relative spending on technology versus human capital, and what the United States needs to do to win the AI race. In our most recent paper, we shifted our analysis to focus on the commonalities of different plans across countries. We wanted to determine if culturally similar countries adopted similar approaches to addressing AI. In that paper, we uncovered relatively few similarities across culturally similar countries and speculated that this might be due to the relative immaturity of AI.

The French President, Emmanuel Macron, delivering a speech on “Artificial intelligence for humanity” at the College de France, in which he presented the French strategy on AI. College of France, Paris, France, March 29, 2018. Credit: Arthur Nicholas Orchard / Hans Lucas via Reuters Connect

However, we did uncover startling differences between how Western nations and China were approaching AI. In the West, countries—with the U.S. leading—are largely focused on the dangers of AI and are working hard to ensure that appropriate “guardrails” are being put in place to ensure that the technology is properly managed from the onset. By contrast, China is almost exclusively focused on research and development (R&D) and is devoting very little energy to constraining possible negative outcomes from AI development. This creates a situation where China, already in the lead on AI, has the capability to further extend its lead, while the U.S. and other countries build guardrails.

In this paper, we continue to uncover similarities in how different countries are approaching different aspects of AI development and to examine what countries are most similar (and most dissimilar) in their approaches and then link these clusters to national AI performance.

As with our recent blog post, we have included the 34 countries that have produced and published a national AI strategy: Australia, Austria, Belgium, Canada, China, Czechia, Denmark, Estonia, Finland, France, Germany, India, Italy, Japan, South Korea, Lithuania, Luxembourg, Malta, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, Qatar, Russia, Serbia, Singapore, Spain, Sweden, the U.A.E., the U.K., Uruguay, and the U.S.

In our previous paper, we studied data management, algorithmic management, AI governance, research and development (R&D) capacity development, education capacity development, and public service reform capacity development. For the current paper, we have used the first three strategy aspects, grouped the three forms of capability development into a single index, and added separate industry and public services measures. Therefore, in the current study we examine the following six aspects that are part of each strategic AI plan:

  • Data management refers to how the country envisages capturing and using the data derived from AI. Data management is composed of the sub-elements of data exchange between governmental entities, with other stakeholders, and with other nations, exchange regulations, data privacy, and data security.
  • Algorithmic management refers to a country’s awareness of algorithmic issues or algorithmic ethics. There are four components to algorithmic management: ethics, bias, transparency, and trust.
  • AI governance refers to the inclusivity, transparency, and public trust in AI and the need for appropriate oversight. AI governance involves AI security, regulations, social inequality impact, risks, intellectual property rights protection, and interoperability.
  • Capacity development refers to the sources that each country is going to use to develop its AI. There are 14 sources to develop these AI skills: multisector R&D, R&D funding, R&D institutes, primary and secondary education sources, higher education, lifelong learning, vocational, SMEs and startups, tax incentives, procurement, business model innovation, pilot projects, and attracting experts.
  • Industries reflect private sector industries that are targets of the national AI effort. There are eight industries that are mentioned in the strategic plans: financial, manufacturing, defense, technology, energy and natural resources, agriculture, health care, and tourism.
  • Public services refer to the administrative functions of public service that AI is intended to address. In total, 11 foci of public service were mentioned in the plans: immigration, courts, public safety, revenue/tax, education, environmental, defense, transportation, healthcare, and ICT.

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