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The growing integration of Artificial Intelligence (AI) into public decision-making is reshaping how institutions operate and how policies are designed, implemented and evaluated. AI instruments are increasingly used across multiple policy domains, such as justice, healthcare, and urban governance. They can thus shape policy outcomes, alter institutional responsibilities and transform relations between citizens and public authorities. This raises important questions about the changing tools and processes of public administration.
Algorithmic systems promise greater efficiency and predictive capacity, yet they also raise questions about transparency, accountability, and the practical workability of AI within existing policy processes. While existing scholarship has examined AI governance and its regulatory frameworks, less attention has been paid to its effects on institutional trust - understood here as citizens' perceptions of the legitimacy and reliability of institutions that design and implement policy. Within this framework, a key unresolved issue is the distinction between trust in technological systems and trust in the institutions that adopt them. AI may strengthen institutional credibility by reducing arbitrariness and improving consistency of administrative performance. Conversely, algorithmic opacity and the perceived exclusion of citizens from decision-making may undermine trust and democratic legitimacy. The effects of AI are therefore likely to depend on how it is embedded in policy-making processes.
Against this backdrop, this panel invites theoretical and empirical contributions examining how AI reshapes policy-making, public administration, and institutional trust. We particularly welcome studies on AI across different stages of the policy cycle, from design to street-level implementation; bureaucratic discretion and administrative decision-making; algorithmic transparency and accountability; public perceptions of AI-driven policy tools; and comparative analyses across institutional and national contexts. Comparative, case-based, quantitative, qualitative and mixed-methods research is equally encouraged. We invite scholars to submit paper proposals addressing any of the themes outlined above, and we especially welcome contributions from early-career researchers and from a diverse range of geographical and disciplinary backgrounds.