Computational Social Science Seminar: Xiaofan Liang, U-M Taubman College Architecture and Urban Planning
Title: Decoding the Municipal AI Landscape: Generative AI-Driven Insights for AI Governance and Application in the City of Seattle
Abstract
With the rapid progression of AI development, local governments have started to regulate, govern, and apply AI technologies in the public sector. However, tracking these conversations and advancements across cities is difficult because there is no structured data and standards. We explore a novel human-AI collaboration workflow for identifying and categorizing public documents related to AI governance and application, using the City of Seattle as a case study. Our goal is to move beyond simple keyword searches to accurately detect predefined AI topics, retrieve supporting evidence, and minimize hallucinations common in end-to-end LLM applications. The ultimate vision is to enable the public to find relevant AI topics in a large volume of public documents and provide a mechanism to trace back to the original supporting texts. We applied our methods to all public documents released by the City of Seattle to test validity and successfully identified ground-truth AI use cases and governance methods. We envisioned that this method can be the backbone of a knowledge infrastructure that facilitates lessons learned across municipalities.
About the CSS seminar series
The University of Michigan School of Information’s Computational Social Science seminar series brings together a vibrant and diverse community of scholars whose cutting-edge research in information science, computer science and the social sciences aims to broaden our understanding of important social and technological issues.
The organizer for the Fall 2025 series is UMSI assistant professor Sabina Tomkins.
Featured Speaker
Xiaofan Liang
U-M Taubman College Architecture and Urban Planning
Dr. Xiaofan Liang is an Assistant Professor of Urban Planning at the University of Michigan's Taubman College of Architecture and Urban Planning. She is also affiliated with the University of Michigan's Center for the Study of Complex Systems and Michigan Institute of Data and AI in Society. She earned her Master’s and PhD in Urban and Regional Planning from the Georgia Institute of Technology (2023), a Bachelor’s degree in Computer Science from Minerva University (2019), and a Bachelor’s degree in Sociology from the University of California, Berkeley (2015).
Her research interests focus on urban networks and the application and implication of new technologies and data in urban planning and governance. Most recently, she is leading a funded project on using AI to study zoning codes for data centers and renewable energy sitting, studying why it is so hard to travel in the NYC subway network from ADA perspectives, and writing a pedagogical paper on what and how to teach urban planners in the era of AI. She is also actively looking for a PhD to start Fall 2026.
Contact: [email protected]