The growing and transformative presence of artificial intelligence (AI) in urban planning has intensified both opportunities and risks, particularly for smart city planning. However, no standardized framework exists for assessing these risks across city systems. This research examines how smart city AI risk is currently evaluated throughout American smart cities, and explores whether AI integration is reshaping the foundational concept of the smart city or not. Using a qualitative, cross-case analysis of four U.S. smart cities (Seattle, San Francisco, New York City, and Washington, DC), this study identifies patterns in AI deployment across the six dimensions of smart cities and evaluates the consistency and scope of municipal AI risk evaluation practices. Findings revealed an overall imbalance in both dimensional AI deployment and risk consideration, with disproportionate emphasis on cybersecurity and data privacy, while risk areas such as integration, monitoring, equity, and resilience remain underdeveloped. Based on these findings, this research proposes a standardized Smart City AI Risk Framework designed to support policy alignment and holistic AI risk evaluation flexibly across smart city contexts. Insufficient evidence supporting AI urbanism as a distinct planning movement separate from smart cities is found, framing smart city AI as a supportive decisionmaking tool for city agencies, rather than an autonomous decision maker. Ultimately, the research advocates for a systematic approach to responsible AI usage in smart city planning.
Michael Selfridge (Thu,) studied this question.