The digitization of planning and other urban governance processes is an unstoppable trend in virtually all countries in the world. Cash-strapped governments and agencies need to optimize resources, and the digital transition is the subject of intensive national and global funding.

Big data, machine learning and generative artificial intelligence create possibilities such as affordability, scope and speed, but also significant challenges, like lack of technical capacity and knowledge, lack of funds to implement new solutions, security of technical jobs, ethics, and data protection, to name a few.

These opportunities and challenges have been the subject of intensive academic research. But there is insufficient research in the area of urban governance, to guide practices in a rapidly changing technological environment.

To address these challenges, the Urban Studies Foundation is sponsoring a seminar series aimed at promoting discussion among academics and urban governance practitioners on the development of a research agenda for new, responsible and transparent AI capabilities for accessible decision support for urban governance — an agenda that responds to the needs of stakeholders, supported by evidence from urban sciences, and engaged with technologists and technology developers.

This agenda will feature training sessions for early career researchers, and be open to other academics and practitioners. It will be disseminated among wider audiences via webinars and policy briefs.​​

The series will have three key events:
• Dec. 8-10: “What Is AI for Urban Governance” and “AI for Advanced Urban Simulation Methods,” at Suffolk University in Boston (Register online)
• April 8-10, 2026: “AI as a Tool for Inclusive DS” and “AI for Participatory Methods,” at University of Brasília in Brazil
• July 22-24, 2026: A co-produced research agenda for responsible and transparent AI for DS in urban governance, and a methodological toolkit to include responsible and transparent AI in urban governance research, at the University of Manchester in England

A series of webinars and policy briefs will complement the events, starting with a launch webinar that was held on Oct. 24.

The series will bring together urban academics and urban governance practitioners in the three countries, and the follow-up webinars and policy briefs will disseminate the findings globally to wider communities.

Policy briefs will be translated into multiple languages.

The series has the support of the Massachusetts Municipal Association (USA), the National Federation of Mayors (Brazil), and the Local Government Information Unit (UK), who will provide support in mobilizing the local and national communities of urban stakeholders and in disseminating the findings of the seminar series.

The seminar series is expected to generate a forum of discussion with academics and urban stakeholders to address the fast pace of GenAI development in urban governance. It will then create a large community of academics and urban stakeholders engaged in research and practices in urban governance where GenAI is presenting itself as a central future avenue of development.

By coproducing a research agenda with practice implications in the context of three countries, all leading on urban research and with different regulatory practices, the seminar series is expected to give a robust contribution to the discussion about the development and use of AI in urban governance.

The co-produced agenda (in the form of an academic article and a series of policy briefs) is expected to help the academic and practice communities to navigate — with more conceptual, technical and policy confidence — the new spaces created by GenAI in urban governance.

The series is also expected to create a community of academics and practitioners in urban governance who will take the discussions on and develop research and policy initiatives.

Click here for more information about the seminar series. Questions may be emailed to [email protected].

Written by Carlos Rufin, Ph.D., a senior lecturer at Suffolk University

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