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Integrative urban AI to expand coverage, access, and equity of urban data.

Bill HoweJackson Maxfield BrownBin HanBernease HermanNic WeberAn YanSean YangYiwei Yang
Published in: The European physical journal. Special topics (2022)
We consider the use of AI techniques to expand the coverage, access, and equity of urban data. We aim to enable holistic research on city dynamics, steering AI research attention away from profit-oriented, societally harmful applications (e.g., facial recognition) and toward foundational questions in mobility, participatory governance, and justice. By making available high-quality, multi-variate, cross-scale data for research, we aim to link the macrostudy of cities as complex systems with the reductionist view of cities as an assembly of independent prediction tasks. We identify four research areas in AI for cities as key enablers: interpolation and extrapolation of spatiotemporal data, using NLP techniques to model speech- and text-intensive governance activities, exploiting ontology modeling in learning tasks, and understanding the interaction of fairness and interpretability in sensitive contexts.
Keyphrases
  • artificial intelligence
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  • big data
  • working memory
  • global health
  • healthcare
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  • deep learning
  • affordable care act