WEIRD AI: Understanding what nations include in their artificial intelligence plans

The artificial intelligence robot, AI-MATHS, developed by Chengdu Zhunxingyunxue Technology, which takes part in the math test during the 2017 National College Entrance Exam, also known as Gaokao, is pictured in Chengdu city, southwest China's Sichuan province, 7 June 2017. A robot sat for the math test during China's national college entrance exam in the southwestern city of Chengdu on Wednesday (7 June 2017). The robot, AI-MATHS, consisting of 11 servers, was developed by Chengdu Zhunxingyunxue Technology. It completed two versions of the exam's math test on Wednesday afternoon. There are several versions of the tests in different regions of China. The robot finished the Beijing test paper in 22 minutes, scoring 105 points out of 150 points, without Internet support. It scored 100 points on another version of the test. "It would take two hours for a human to finish the test. I hope next year it can improve its performance on logical reasoning and computer algorithms and score over 130," said Lin Hui, company CEO. In February, the robot scored 93 on one math test, slightly higher than the passing grade of 90. The company participated in a project of China's Ministry of Science and Technology, which plans to develop gaokao robots. Under the plan, by 2020, AI robots will be smart enough to gain admission to leading universities such as Peking University and Tsinghua University through the entrance exam.No Use China. No Use France.

In 2021 and 2022, the authors published a series of articles on how different countries are implementing their national artificial intelligence (AI) strategies. In these articles, we examined how different countries view AI and looked at their plans for evidence to support their goals. In the later series of papers, we examined who was winning and who was losing in the race to national AI governance, as well as the importance of people skills versus technology skills, and concluded with what the U.S. needs to do to become competitive in this domain.

Since these publications, several key developments have occurred in national AI governance and international collaborations. First, one of our key recommendations was that the U.S. and India create a partnership to work together on a joint national AI initiative. Our argument was as follows: “…India produces far more STEM graduates than the U.S., and the U.S. invests far more in technology infrastructure than India does. A U.S. -India partnership eclipses China in both dimensions and a successful partnership could allow the U.S. to quickly leapfrog China in all meaningful aspects of A.I.” In early 2023, U.S. President Biden announced a formal partnership with India to do exactly what we recommended to counter the growing threat of China and its AI supremacy.

Second, as we observed in our prior paper, the U.S. federal government has invested in AI, but largely in a decentralized approach. We warned that this approach, while it may ultimately develop the best AI solution, requires a long ramp up and hence may not achieve all its priorities.

Finally, we warned that China is already in the lead on the achievement of its national AI goals and predicted that it would continue to surpass the U.S. and other countries. News has now come that China is planning on doubling its investment in AI by 2026, and that the majority of the investment will be in new hardware solutions. The U.S. State Department also is now reporting that China leads the U.S. in 37 out of 44 key areas of AI.  In short, China has expanded its lead in most AI areas, while the U.S. is falling further and further behind.

Considering these developments, our current blog shifts findings away from national AI plan achievement to a more micro view of understanding the elements of the particular plans of the countries included in our research, and what drove their strategies. At a macro level, we also seek to understand if groups of like-minded countries, which we have grouped by cultural orientation, are taking the same or different approaches to AI policies. This builds upon our previous posts by seeking and identifying consistent themes across national AI plans from the perspective of underlying national characteristics.

Six Key Elements of National Plans

In this blog, the countries that are part of our study include 34 nations that have produced public AI policies, as identified in our previous blog posts: Australia, Austria, Belgium, Canada, China, Czechia, Denmark, Estonia, Finland, France, Germany, India, Italy, Japan, South Korea, Lithuania, Luxembourg, Malta, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Qatar, Russia, Serbia, Singapore, Spain, Sweden, UAE, UK, Uruguay, and USA.

For each, we examine six key elements in these national AI plans—data management, algorithmic management, AI governance, research and development (R&D) capacity development, education capacity development, and public service reform capacity development—as they provide insight into how individual countries approach AI deployment. In doing so, we examine commonalities between culturally similar nations which can lead to both higher and lower levels of investment in each area.

We do this by exploring similarities and differences through what is commonly referred to as the WEIRD framework, a typology of countries based on how Western, Educated, Industrialized, Rich, and Democratic they are. In 2010, the concept of WEIRD-ness originated with Joseph Henrich, a professor of human evolutionary biology at Harvard University. The framework describes a set of countries with a particular psychology, motivation, and behavior that can be differentiated from other countries. WEIRD is, therefore, one framework by which countries can be grouped and differentiated to determine if there are commonalities in their approaches to various issues based on similar decision-making processes developed through common national assumptions and biases.

Below are our definitions of each element of national AI plans, followed by where they fall along the WEIRD continuum.

Plan elements

Data management refers to how the country envisages capturing and using the data derived from AI. For example, the Singapore plan defines data management defines “[a]s the nation’s custodian of personal and administrative data, the Government holds a data resource that many companies find valuable. The Government can help drive cross-sectoral data sharing and innovation by curating, cleaning, and providing the private sector with access to Government datasets.

Algorithmic management addresses the country’s awareness of algorithmic issues. For example, the German plan states that: “[t]he Federal Government will assess how AI systems can be made transparent, predictable and verifiable so as to effectively prevent distortion, discrimination, manipulation and other forms of improper use, particularly when it comes to using algorithm-based prognosis and decision-making applications.

AI governance refers to the inclusivity, transparency and public trust in AI and the need for appropriate oversight. The language in the French plan asserts: “[i]n a world marked by inequality, artificial intelligence should not end up reinforcing the problems of exclusion and the concentration of wealth and resources. With regards to AI, a policy of inclusion should thus fulfill a dual objective: ensuring that the development of this technology does not contribute to an increase in social and economic inequality; and using AI to help genuinely reduce these problems.

Overall, capacity development is the process of acquiring, updating and reskilling human, organizational and policy resources to adapt to technological innovation. We examine three types of capacity development – R&D, Education, and Public Service Reform.

R&D capacity development focuses on government incentive programs for encouraging private sector investment in AI. For example, the Luxembourg plan states: “[t]he Ministry of the Economy has allocated approximately €62M in 2018 for AI-related projects through R&D grants, while granting a total of approximately €27M in 2017 for projects based on this type of technology. The Luxembourg National Research Fund (FNR), for example, has increasingly invested in research projects that cover big data and AI-related topics in fields ranging from Parkinson’s disease to autonomous and intelligent systems – approximately €200M over the past five years.

Education capacity development focuses on learning in AI, at the post-secondary, vocational and secondary levels. For example, the Belgian plan states: “Overall, while growing, the AI offering in Belgium is limited and insufficiently visible. [W]hile university-college PXL is developing an AI bachelor programme, to date, no full AI Master or Bachelor programmes exist.”

Public service reform capacity development focuses on applying AI to citizen-facing or –supporting services. For example, the Finnish plan states: “Finland’s strengths in piloting [AI projects] include a limited and harmonised market, neutrality, abundant technology resources and support for legislation. Promoting an experimentation culture in public administration has brought added agility to the sector’s development activities.

WEIRD-ness: Being Western, Educated, Industrialized, Rich, and Democratic

In the next step of our analysis, we identify the level of each country and then group countries by their WEIRD-ness. Western uses the World Population Review’s definition of the Latin West, and is defined by being in or out of this group, which is a group of countries sharing a common linguistic and cultural background, centered on Western Europe and its post-colonial footprint. Educated is based on the mean years of schooling in the UN Human Development Index, where 12 years (high school graduate) is considered the dividing point between high and low education. Industrialized adopts the World Bank industry value added of GDP, where a median value of $3500 USD per capita of value added separates high from low industrialization. Rich uses the Credit Suisse Global Wealth Databook mean wealth per adult measure, where $125k USD wealth is the median amongst countries. Democratic applies the Democracy Index of the Economist Intelligence Unit, which differentiates between shades of democratic and authoritarian regimes and where the midpoint of hybrid regimes (5.0 out of 10) is the dividing point between democratic and non-democratic. For example, Australia, Austria, and Canada are considered Western, while China, India and Korea are not. Germany, the U.S., and Estonia are seen as Educated, while Mexico, Uruguay and Spain are not. Canada, Denmark, and Luxemburg are considered Industrialized, while Uruguay, India and Serbia are not. Australia, France, and Luxembourg are determined to be Rich while China, Czechia and India are not. Finally, Sweden, the UK and Finland are found to be Democratic, while China, Qatar and Russia are not.

Figure 1 maps the 34 countries in our sample as follows. Results ranged from the pure WEIRD countries, including many Western European nations and some close trading partners and allies such as the United States, Canada, Australia, and New Zealand.

Figure 1: Countries classified by WEIRD framework[1]

A group of 34 countries' names sorted into boxes based on what permutation of the attributes Western, Educated, Industrialized, and Democratic they have.

By comparing each grouping of countries with the presence or absence of our six data elements (data management, algorithmic management, AI governance, and R&D capability development), we can understand how each country views AI alone and within its particular grouping. For example, wEIRD Japan and Korea are high in all areas except for western and both invest highly in R&D capacity development but not education capacity development.

Correlations between WEIRD framework and AI strategies

The methodology used for this blog was Qualitative Configuration Analysis (QCA), which seeks to identify causal recipes of conditions related to the occurrence of an outcome in a set of cases. In QCA, each case is viewed as a configuration of conditions (such as the five elements of WEIRD-ness) where each condition does not have a unique impact on the outcome (an element of AI strategy), but rather acts in combination with all other conditions. Application of QCA can provide several configurations for each outcome, including identifying core conditions that are vital for the outcome and peripheral conditions that are less important. The analysis for each plan element is described below.

Data management has three different configurations of countries that have highly developed plans. In the first configuration, for WeIRD countries—those that are Western, Industrialized, Rich, and Democratic (but not Educated; e.g., France, Italy, Portugal, and Spain)—being Western was the best predictor of having data management as part of their AI plan, and the other components were of much less importance. Of interest, not being Educated was also core, making it more likely that these countries would have data management as part of their plan. This would suggest that these countries recognize that they need to catch up on data management and have put plans in place that exploit their western ties to do so.

In the second configuration, which features WEIrD Czechia, Estonia, Lithuania, and Poland, being Democratic was the core and hence most important predictor and Western, Educated, and Industrialized were peripheral and hence less important. Interestingly, not being Rich made it more likely to have this included. This would suggest that these countries have developed data management plans efficiently, again leveraging their democratic allies to do so.

In the third and final configuration, which includes the WeirD countries of Mexico, Serbia, Uruguay, and weirD India, the only element whose presence mattered was the level of Democracy. That these countries were able to do so in low wealth, education, and industrialization contexts demonstrates the importance of investment in AI data management as a low-cost intervention in building AI policy.

Taken together, there are many commonalities, but a country being Western and/or Democratic were the best predictors of a country having a data governance strategy in its plan. In countries that are Western or Democratic, there is often a great deal of public pressure (and worry) about data governance, and we suspect these countries included data governance to satisfy the demands of their populace.

We also examined what conditions led to the absence of a highly developed data management plan. There were two configurations that had consistently low development of data management. In the first configuration, which features wEIrd Russian and UAE and weIrd China, being neither Rich nor Democratic were core conditions. In the second configuration, which includes wEIRD Japan and Korea, core conditions were being not Western but highly Educated. Common across both configurations was that all countries were Industrialized but not Western. This would suggest that data management is more a concern of western countries than non-western countries, whether they are democratic or not.

However, we also found that the largest grouping of countries—the 15 WEIRD countries in the sample—were not represented, falling neither in the high or low configurations. We believe that this is due to there being multiple different paths for AI policy development and hence they do not all stress data governance and management. For example, Australia, the UK, and the US have strong data governance, while Canada, Germany and Sweden do not. Future investigation is needed to differentiate between the WEIRDest countries.

For algorithmic management, except for WeirD Mexico, Serbia, and Uruguay, there was no discernable pattern in terms of which countries included an acknowledgment of the need and value of algorithmic management. We had suspected that more WEIRD countries would be sensitive to this, but our data did not support this belief.

We examined the low outcomes for algorithmic management and found two configurations. The first was wEIRD Japan and Korea and weIRD Singapore, where the core conditions were being not Western but Rich and Democratic. The second was wEIrd Russian and UAE and weIrd China, where the core elements were not Rich and not Democratic. Common across the two configurations with six countries was being not Western but Industrialized. Again, this suggests that algorithmic management is more a concern of western nations than non-western ones.

For AI governance, we again found that, except for WeirD Mexico, Serbia, and Uruguay, there was no discernable pattern for which countries included this in their plans and which countries did not. We believed that AI governance and algorithmic management to be more advanced in WEIRD nations and hence this was an unexpected result.

We examined the low outcomes for AI governance and found three different configurations. The first was wEIRD Japan and Korea and weIRD Singapore, where the core conditions were being not Western but Rich and Democratic. The second was wEIrd Russian and UAE, where the core elements were not Western but Educated. The third was weirD India, where the core elements were being not Western but Democratic. Common across the three configurations with six countries was not being of western classification. Again, this suggests that AI governance is more a concern of western nations than nonwestern ones.

There was a much clearer picture of high R&D development, where we found four configurations. The first configuration was the 15 WEIRD countries plus the WEIrD ones—Czechia, Estonia, Lithuania, Poland. For the latter, while they are not some of the richer countries, they still manage to invest heavily in developing their R&D.

The second configuration included WeirD Mexico, Serbia, Uruguay, and weirD India. Like data governance, these countries were joined by their generally democratic nature but lower levels of education, industrialization, and wealth.

Conversely, the third configuration included the non-western, non-democratic nations such as weIRd Qatar and weIrd China. This would indicate that capability development is of primary importance for such nations at the expense of other policy elements. The implication is that investment in application of AI is much more important to these nations than its governance.

Finally, the fourth configuration included the non-western but democratic nations such as wEIRD Japan, Korea, and weIRD Singapore. This would indicate that the East, whether democratic or not, is as equally focused on capability development and R&D investment as the West.

We did not find any consistent configurations for low R&D development across the 34 nations.

For high education capacity development, we found two configurations, both with Western but not Rich core conditions. The first includes WEIrD Czechia, Estonia, Lithuania, and Poland while the second includes WeirD Mexico, Serbia, and Uruguay. Common conditions for these seven nations were being Western and Democratic, but not Rich, while the former countries were Educated and Industrialized, while the latter were not. These former eastern-bloc and colonial nations appear to be focusing on creating educational opportunities to catch up with other nations in the AI sphere.

Conversely, we found three configurations of low education capacity development. The first includes wEIRD Japan and Korea and weIRD Singapore, representing the non-Western but Industrialized, Rich, and Democratic nations. The second was weIRd Qatar, not Western or Democratic but Rich and Industrialized, while the third was wEIrd Russia and UAE. The last was weirD India, being Democratic but low in all other areas. The common factor across these countries was being non-western, demonstrating that educational investment to improve AI outcomes is a primarily western phenomenon, irrespective of other plan elements.

We did not find any consistent configurations for high public service reform capacity development, but we did find three configurations for low investment in such plans. The first includes wEIRD Japan and Korea, the second was weIRd Qatar, and the last was weirD India. This common core factor across these three configurations was that they were not western countries, further highlighting the different approaches taken by western and nonwestern countries.


Overall, we expected more commonality in which countries included certain elements, and the fragmented nature of our results likely reflects a very early stage of AI adoption and countries simply trying to figure out what to do. We believe that, over time, WEIRD countries will start to converge on what is important and those insights will be reflected in their national plans.

There is one other message that our results pointed out: the West and the East are taking very different approaches to AI development in their plans. The East is almost exclusively focused on building up its R&D capacity and is largely ignoring the traditional “guardrails” of technology management (e.g., data governance, data management, education, public service reform). By contrast, the West is almost exclusively focused on ensuring that these guardrails are in place and is spending relatively less effort on building the R&D capacity that is essential to AI development. This is perhaps the reason why many Western technology leaders are calling for a six-month pause on AI development, as that pause could allow suitable guardrails to be put in place. However, we are extremely doubtful that countries like China will see the wisdom in taking a six-month pause and will likely use the pause to create even more space between their R&D capacity and the rest of the world. This “all gas, no brakes” Eastern philosophy has the potential to cause great global harm but will undeniably increase their domination in this area. We have little doubt about the need for suitable guardrails in AI development but are also equally convinced that a six-month pause is unlikely to be honored by China. Because of China’s lead, the only prudent strategy is to build the guardrails while continuing to engage in AI development. Otherwise, the West will continue to fall further behind, resulting in the development of a great set of guardrails but with nothing of value to guard.

[1] A capital letter denotes being high in an element of WEIRD-ness while a lowercase letter denotes being low in that element. For example, “W” means western while “w” means not western. (Back to top)