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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 12·5 min read
Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis
TL;DR
A comprehensive analysis of 74 national and 3 regional AI strategies reveals strong horizontal convergence on economic and ethical goals, while highlighting persistent divergence in human rights and governance approaches. This study provides valuable insights for policymakers in shaping future AI strategies.
✦ Why It Matters
Review your AI strategy to align with emerging global norms and address human rights concerns.
Key Takeaways
- Analyze your AI strategy for alignment with global trends.
- Focus on economic competitiveness and ethical AI use.
- Address gaps in human rights and governance approaches.
- Utilize findings to inform future AI policy development.
Full Summary
Governments globally are developing AI strategies in response to rapid advancements in artificial intelligence. This study analyzes 74 national and 3 regional AI strategies from 205 countries, focusing on their policy design elements: goals, approaches, and principles.
Using a latent-inductive coding method, the research investigates horizontal convergence (country-to-country) and vertical convergence (region-to-country) over time. Findings indicate significant horizontal convergence in areas like economic competitiveness and ethical AI use, but notable divergence in human rights and participatory governance.
Regionally, the African Union shows the highest vertical convergence, while the European Union aligns on regulatory priorities but diverges on human-centric values. These insights equip policymakers with a framework for understanding emerging norms in AI policy design.