AI for public good: reorientating fairness, accountability and uncertainty in public sector governance

First, it develops conceptual clarity by mapping how dominant AI paradigms embed implicit value commitments that frequently conflict with governance norms. Second, it provides empirical insight into how such systems operate in practice, drawing on case studies and technical experiments involving fairness-aware algorithms and generative models. Third, it advances an institutional analysis demonstrating that alignment with public values must extend beyond predictive performance to encompass the procedural and normative conditions that sustain legitimacy.<br><br>The central argument advanced is that achieving “AI for public good” requires more than technical refinement.

It demands a reorientation of both AI system design and the governance frameworks that oversee their deployment. This includes developing context-sensitive evaluation methodologies, embedding accountability architectures, and recognising uncertainty not as a technical failure but as an intrinsic feature of democratic decision-making.<br><br>By integrating technical, conceptual, and institutional analysis, this thesis demonstrates that interdisciplinary engagement is indispensable to shaping AI’s role in public life, one that strengthens, rather than undermines, the legitimacy of public institutions.<br>