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Paper short abstract

This paper examines how inequalities in AI knowledge production shape governance debates. Using original global co-authorship data, it shows how limited epistemic voice in the Global South risks reinforcing inequitable and developmentally misaligned AI regulation.

Paper long abstract

Recent debates on artificial intelligence governance increasingly emphasise ethical frameworks and regulatory principles as central tools for managing AI’s societal impacts. However, such approaches emerge under conditions of profound inequality in the production and circulation of technological knowledge. This paper examines how uneven participation in AI research shapes the epistemic foundations of AI governance and raises questions about the developmental relevance of emerging regulatory agendas, particularly their alignment with local capacities, priorities, and institutional contexts.

The analysis draws on an original dataset of AI-related research co-authorship from 2013 to 2022, disaggregated across key AI fields including Robotics, Natural Language Processing, Computer Vision, Large Language Models, and AI Safety. Collaboration patterns are analyzed based on countries’ belonging to the Global North or Global South and geopolitical groupings (United States, China, Europe, and Others). The findings reveal a persistent concentration of AI research within high-income countries, alongside highly asymmetric cross-regional collaborations. Field-level analysis further shows that AI Safety remains a comparatively less developed area of AI knowledge production, indicating potential entry points for broader participation by the Global South.

Participation in knowledge production matters because it shapes problem definitions, risk perceptions, and the normative assumptions that inform regulatory agendas. The paper argues that AI governance is shaped not only through formal regulatory processes, but through historically embedded and asymmetrical power relations, with knowledge production serving as a key observable dimension. The paper concludes by outlining alternative pathways for AI governance that foreground epistemic inclusion, capacity-building, and the strengthening of regional research ecosystems.