Supranational bodies define key themes of AI success and governance
What Does AI Success Look Like? A Mixed-Methods Analysis of Supranational AI Governance Instruments
Computers and Society
Summary
Many international organizations have created rules and guidelines to manage how AI is developed and used. This paper looks closely at what these groups say 'AI success' means in their official documents. The authors found common themes and created a new way to categorize them, suggesting that AI governance should move from vague principles to better coordination. They also offer ideas for future policies to improve how AI is governed worldwide.
What this means in practice
- •For policy makers: Develop more coordinated AI governance strategies by using the categorization of AI success themes identified in supranational documents.
- •For compliance teams: Align organizational AI use policies with supranational AI governance trends to anticipate regulatory expectations.
A survey. It maps existing work.
Authors
Delaram Golpayegani, Marta Lasek-Markey, Monique Munarini, Arjumand Younus, Aphra Kerr, Dave Lewis
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.