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Title	Predicting crime is easy, using crime predictions is hard
Creator	George Mohler
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-03-20T09:32
Description	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.
Extent	36.0 minutes
Subject	Mathematics Game theory, economics, social and behavioral sciences Computer science
Geographic Location	Banff (Alta.)
Type	Moving Image
FileFormat	video/mp4
Language	eng
Notes	Author affiliation: Indiana University–Purdue University Indianapolis
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-09-17
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0380897
URI	http://hdl.handle.net/2429/71697
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Researcher
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
AggregatedSourceRepository	DSpace

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