Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis
This paper analyzes 74 national and 3 regional AI strategies to reveal strong horizontal convergence on economic and research priorities alongside persistent divergence in human rights and governance, while identifying varying degrees of vertical alignment between national strategies and their respective regional frameworks.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine the world of government policy as a massive, global potluck dinner. For decades, countries have brought their own unique dishes to the table, but lately, everyone is suddenly obsessed with the same new ingredient: Artificial Intelligence (AI). When a new, powerful technology like AI arrives, it's like a surprise guest who can cook, clean, and drive a car all at once. Governments everywhere are scrambling to write "AI Strategies"—basically, rulebooks for how to handle this guest. But here's the big question: Are all these countries writing the exact same rulebook, or are they each cooking up something totally different? To answer this, we need to understand three simple things. First, Policy Goals are the "what" (what the country wants to achieve, like getting rich or staying safe). Second, Policy Approaches are the "how" (the specific tools they use, like giving money to scientists or making new laws). Third, Policy Principles are the "why" and the "rules of the road" (the values they promise to keep, like being fair or honest). If everyone is converging, they are all agreeing on the menu. If they are diverging, they are arguing over the ingredients. This matters because if countries are all doing the same thing, it's easier to trade and talk to each other. But if they are doing totally different things, it could get messy.
This paper is like a super-organized food critic who decided to taste-test every single AI rulebook in the world to see if the chefs are copying each other or going rogue. The researchers, Benjamin Faveri and Brie Bhasin, gathered a massive dataset of 74 national AI strategies from countries all over the globe, plus 3 regional strategies (like a menu for the whole European Union, the African Union, and the Nordic-Baltic region). They didn't just read the titles; they broke every document down into its tiny building blocks—the goals, the approaches, and the principles—to see if the recipes were becoming more similar (converging) or more different (diverging) over time.
Here is what they found, and it's a bit of a mixed bag. On the one hand, there is a huge amount of agreement on the "basics." Almost every country (71 out of 74) agreed that they need to support AI research. It's like every chef agreeing that you need a good stove. Similarly, nearly everyone (68 out of 74) agreed that AI should be used ethically. This suggests that if you are writing a new AI strategy today, you basically have to include research funding and ethical rules, or people will think your strategy is broken. They are also all agreeing on the goal of becoming an "AI leader" and building up their national skills.
However, the paper suggests that when you look deeper, the countries start to diverge, or drift apart, in some very important ways. While they all agree on the "stove," they can't agree on the "flavor." Specifically, countries are very different when it comes to human rights, making sure the public gets a say in decisions, and principles like diversity and inclusion. Some countries put these human-centric values front and center, while others barely mention them. The authors suggest this creates a weird situation called "divergence within convergence": everyone is saying the same high-level words about being "ethical," but they aren't actually doing the same things to make it happen.
The paper also looked at how countries in the same "neighborhood" (regions) compare to their regional leaders. The African Union (AU) showed the strongest agreement, with member countries almost perfectly mirroring the regional plan. The European Union (EU) was a bit more complicated: its countries agreed strongly on the economic and legal rules but started to drift apart on human values like diversity. The Nordic-Baltic region was the most mixed, with countries agreeing on big ethical ideas but disagreeing on the actual daily work.
Ultimately, the paper suggests that the window for countries to invent their own unique, independent AI strategies is closing. Because so many countries are now copying the same early ideas about research and ethics, new countries have less room to be different. The authors warn that if countries keep writing strategies that sound great on paper (saying they care about ethics) but don't actually include the specific rules to make it real, they might lose the public's trust. It's like promising a delicious, healthy meal but serving a burger made of plastic. The study doesn't prove that one specific way is the "best," but it does show us exactly where the world is agreeing and where it is still fighting, giving future policy designers a clear map of what to expect.
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