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arXiv · 2610.31030

What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments

Delaram Golpayegani·Marta Lasek-Markey·Monique Munarini·Arjumand Younus·Aphra Kerr·Dave Lewis
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Abstract

In the last decade and in particular since 2018, supranational institutions have published policies, guidelines, and most recently regulations to govern development and use of Artificial Intelligence (AI). This paper aims to investigate the themes and dimensions of ``AI success'' set forth by instruments of AI governance issued by supranational bodies. To this end, we use a mixed-methods approach, combining manual thematic analysis and automated topic modelling approaches, to analyse a curated corpus of 13 documents issued by supranational bodies, including the European Union bodies, OECD, G20, UNESCO, United Nations, and the Council of Europe between 2018-2025. Based on the analysis and through the lens of sociology of expectations, we propose a novel categorisation scheme for AI success themes emerging from supranational bodies' documents. Drawing on the findings, we advance a set of policy recommendations, setting forward the need to move beyond high-level principles toward more coordinated approaches to AI governance.

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Delaram Golpayegani, Marta Lasek-Markey, Monique Munarini, Arjumand Younus, Aphra Kerr, Dave Lewis. 2026-10-22. What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments. https://arxiv.org/abs/2610.31030

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