📖📖 Paper Synopsis: Integrative urban AI to expand coverage, access, and equity of urban data
Abstract 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 profitoriented, 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 speechand text-intensive governance activities, exploiting ontology modeling in learning tasks, and understanding the interaction of fairness and interpretability in sensitive contexts.
Interpolation of spatiotemporal data using deep learning
Trade-off among distributive and procedural fairness
Hierarchical multi-label classification
Modeling governance behaviors
*image source: https://taubmancollege.umich.edu/urbanplanning/degrees/graduate-certificate-urban-informaticsApril 9th, 2022 by Bin Han