AI and the Transformation of Accountability and Discretion in Urban Governance
This paper is a new, updated version of a previous white paper published by Data-Smart City Solutions. This edition was accepted into the journal Urban Governance on February 21, 2026. We invite you to read this for a revised, peer-reviewed version of our original paper.
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.
About the Author
Stephen Goldsmith
Stephen Goldsmith is the Derek Bok Professor of the Practice of Urban Policy at the Harvard Kennedy School and the director of Data-Smart City Solutions at the Bloomberg Center for Cities at Harvard University. He previously served as the mayor of Indianapolis and deputy major of New York City.
About the Author
Juncheng "Tony" Yang
Juncheng "Tony" Yang is a doctoral candidate at the Harvard Graduate School of Design and a researcher at Data-Smart City Solutions at Harvard Bloomberg Center for Cities. His research focuses on the intersection of institutional arrangements and emerging technologies in “smart city” governance. Additionally, Yang is a Fellow at the Berkman Klein Center for Internet and Society at Harvard Law School. He received a Master of Science in Urbanism from MIT and a Bachelor of Architecture, with distinction and magna cum laude, from Rice University.