A Fork in the Road for Global AI Governance: To Repair or Reconfigure Expert-Led Governance?

Adam McCarthy and Olajide Olugbade

No t72ms_v1, SocArXiv from Center for Open Science

Abstract: Artificial intelligence (AI) has become a live object of global governance, with expert-led institutional arrangements emerging across an international regime complex. While scholarship on epistemic communities and science-policy interfaces treats expertise as a mechanism for coordination under uncertainty, recent work in Objectual International Relations (O-IR) and Science and Technology Studies foregrounds knowledge politics, contested authority, and the co-productive character of governance. We bring these perspectives into conversation with AI governance through a reflexive workshop design using Nominal Group Technique, drawing on two expert workshops in the UK and US. Findings show that expert panels remain valued for structuring debate, coordinating knowledge, and lending legitimacy to policy action. Yet participants identified tensions around exclusion, elite and private capture, weak accountability, geopolitical constraints, and temporal misalignment between institutional processes and technological change. An object-centered analysis further shows that AI remains an unstable governance object; the knowledge required to govern it is contested and unevenly distributed; and expertise itself is politically charged and institutionally unsettled. We synthesize these tensions into two pathways: Repair and Reconfiguration. Repair seeks to stabilize AI as a governable object, standardize relevant knowledge, and secure expert authority through institutional continuity and procedural refinement. Reconfiguration seeks to keep AI contested, broaden credible forms of knowledge, and reshape expertise through participation, reflexivity, and engagement with politics. The framework provides a critical lens for assessing emerging institutional designs for global AI governance and the trade-offs they entail.

Date: 2026-09-02
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:t72ms_v1

DOI: 10.31235/osf.io/t72ms_v1

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