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Predicting crime is easy, using crime predictions is hard

Banff International Research Station for Mathematical Innovation and Discovery

Data science software has been abstracted to the point that a middle or high school student can implement an algorithm to forecast crime hotspots or predict recidivism (or classify images for that matter). The harder questions come after the modeling phase. Can police actually use these algorithms to reduce crime How about "harm" Are these algorithms fair or do they unfairly target certain groups We have anonymous crime tips, is there an analogy to privacy preserving crime models Should these algorithms be black boxes or should they be transparent and restricted in their inputs (like is done in insurance) In this talk I will explore current research on these topics, highlighting some of our contributions and also gaps where further research is needed.

Mathematics; Game theory, economics, social and behavioral sciences; Computer science

video/mp4

Author affiliation: Indiana University–Purdue University Indianapolis

2019-09-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71697

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/