AI and the Transformation of Accountability and Discretion in Urban Governance

TL;DR

This paper conceptualizes how AI transforms discretion and accountability in urban governance, emphasizing redistribution of roles, new risks, and guiding principles for responsible AI integration.

Contribution

It introduces the concept of accountable discretion, offering a framework of principles and measures to guide AI's role in governance accountability.

Findings

AI can strengthen managerial oversight and service delivery.

AI expands citizen access to information and participation.

Risks include data bias, opacity, and fragmented responsibility.

Abstract

This paper offers a conceptual analysis of the transformative role of Artificial Intelligence (AI) in urban governance, focusing on how AI can reshape the relationship between bureaucratic discretion and accountability. Drawing on public administration theory and algorithmic governance research, the study argues that AI does not simply restrict or enhance discretion but redistributes it across institutional levels, professional roles, and citizen interactions. While primarily conceptual, this paper uses illustrative cases to show that AI can strengthen managerial oversight, improve service delivery consistency, and expand citizen access to information. These changes affect different forms of accountability: political, professional, and participatory, while introducing new risks, such as data bias, algorithmic opacity, and fragmented responsibility across actors. In response, the paper introduces the concept of accountable discretion and proposes guiding principles, each linked to actionable measures: equal AI access, adaptive administrative structures, robust data governance, proactive human-led decision-making, and citizen-engaged oversight. This study contributes to the AI governance literature by moving beyond narrow concerns with perceived discretion at the street level, highlighting instead how AI transforms rule-based discretion across governance systems. It also reframes the trade-off between discretion and accountability as a dynamic and evolving relationship shaped by algorithmic systems and institutional practices.