Can AI Achieve the Broken Promises of Smart Cities?

Neil Kleiman, one of the authors behind RethinkAI's report on local governments' AI implementation, spoke with Diana Lind for an article published in her Substack, The New Urban Order.

Last month, the New America RethinkAI coalition, which works with communities to build AI pilots and transforms findings into guidance and policy recommendations, published a new report, “Making AI Work for the Public: An ALT Perspective.” Written by Neil Kleiman, Senior Fellow and Professor, Burnes Center for Social Change, Northeastern University; Eric Gordon, Director, Center for Media Innovation & Social Impact, Boston University; and Mai-Ling Garcia, Director, Emerging Technologies and AI, Bloomberg Centers for Government Excellence and Public Innovation, Johns Hopkins University, the report proposes a new framework for local governments as they integrate AI into their work.

I recently spoke with Kleiman about the report, and what follows is an edited version of our conversation. I used AI to transcribe the interview — AI transcription is a massive times savings for journalists — and as Neil notes, that’s an example of how human intelligence might benefit from AI, not be replaced by it. But will AI truly transform our cities, or will the hype produce another disappointment like the smart cities and civic tech revolution promised to us 15 years ago? I think some of the new city government pilots (see below graphic from the report) point to a hopeful future where we’re using AI to help cities catch up on the very time-consuming work of bureaucracy — I see you, building permits! If AI can help the public sector work at the speed residents want and expect, everyone wins.

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Kleiman: We’re most excited about using this technology to combine community insights with traditional government data. For example, in Dorchester, Mass., a community with very little trust in AI or local government, we created a public common data corpus. We seamlessly combined years of community meeting notes and interviews with traditional crime data, creating a more complete reflection of what crime actually looked like in the neighborhood.