From vision to practice: five years of responsible Urban AI and community insight

Le résumé fourni par la source

Abstract This paper examines how artificial intelligence can be responsibly integrated into urban planning institutions, education, and community decision making. It explores the socio-technical frameworks needed to ensure that Urban AI supports equitable, transparent, and effective planning practice beyond methodological innovation alone. The study synthesizes insights from the fifth Urban AI in Planning roundtable held at the 2025 Association of Collegiate Schools of Planning Annual Conference. Drawing on five years of interdisciplinary dialogue, the paper applies qualitative thematic analysis to discussions among scholars and practitioners in geography, planning, architecture, AI, transportation, and professional design practice. Three interconnected themes emerge. First, Urban AI is most effective when functioning as a decision-support capacity amplifier that complements rather than replaces human judgment. Second, uneven geographies of data availability, privacy expectations, and model performance create disparities across urban, rural, and resource-constrained settings. Third, interdisciplinary collaboration, planning education, and community engagement are essential for developing trustworthy and socially responsive AI systems. Across these themes, persistent concerns include data bias, transparency, governance, and ethical accountability. The paper argues that the future of Urban AI depends not only on technological advancement but also on robust socio-technical frameworks integrating ethical oversight, place-sensitive interpretation, interdisciplinary cooperation, and participatory knowledge production. Responsible Urban AI therefore requires alignment with institutional practices, governance systems, and community values to enhance planning legitimacy, inclusiveness, and long-term societal trust.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.