Purpose To explain why artificial intelligence (AI) governance produces systematically divergent social policy outcomes across regions despite widespread convergence around ethical standards for AI. The study examines how political–economic institutions shape the allocation of social risks, regulatory authority, labour integration and ethics institutionalisation in AI governance, thereby driving these differences. Design/methodology/approach This study uses a comparative qualitative policy analysis based on 24 key AI policy documents published between 2018 and 2025 across the European Union, United States, China, and Indo-Pacific economies. Guided by theory, the documents are systematically coded across four institutional dimensions and converted into simple indices to compare governance approaches across the regions. Findings The findings show clear and systematic differences in how regions govern AI. Five distinct governance models emerge: rights-based (EU), market-driven (US), state-centric (China), hybrid (Australia–Japan–Singapore) and developmental (India). Although many regions use similar ethical language, substantial differences persist in risk allocation, regulatory enforcement, welfare integration and social protection. These differences reflect the historically embedded political–economic institutions shaping each regime. Originality/value The paper reframes AI governance as a form of social policy shaped by political and economic institutions. It develops a new, evidence-based typology of AI governance models and shows that differences across countries are driven by institutional structures and not by ethical principles alone.
Kumar et al. (2026) studied this question.