Responsible AI Governance in the Public Sector: Explaining Contextual Dynamics through a Realist Synthesis Review

Abstract

Responsible AI (RAI) governance is increasingly understood not as a static checklist of principles, but as a dynamic process embedded in institutional, organisational, and sociotechnical contexts. While several ethical frameworks exist, translating high-level principles into situated organisational practices remains challenging. Empirical studies examining how public sector organisations operationalise RAI remain fragmented, limiting cumulative insights. To address this gap, we conduct a realist synthesis review of 21 empirical studies. Our analysis shows that similar interventions in different contexts activate distinct mechanisms and produce divergent outcomes with varying degrees of alignment to RAI principles. From these variations, we identify three cross-cutting dynamics explaining outcomes: organisational embeddedness, power- expertise tensions, and trust-transparency relationships. Together, we term it the situated dynamics of RAI governance. This approach moves beyond asking whether interventions “work” to explain why similar interventions succeed in some contexts and fail in others.