Generating Algorithms for Hot Spots Policing

dc.contributor.advisorHajiaghayi, Mohammad Taghi
dc.contributor.authorVersace, Nathan Rios
dc.contributor.authorArellano, Trina
dc.contributor.authorChen, Alex
dc.contributor.authorDu, Allen
dc.contributor.authorEichstadt, Andrea Maria
dc.contributor.authorLin, Aaron
dc.contributor.authorSamuels, Coley
dc.contributor.authorTao, Grace
dc.contributor.authorTasneem, Zoya
dc.date.accessioned2024-04-17T17:47:15Z
dc.date.available2024-04-17T17:47:15Z
dc.date.issued2024
dc.description.abstractLarge police departments have come to rely on algorithms to predict where crime will occur, such that they can better allocate resources to communities that need them. While these algorithms have been shown to reduce crime, they are not built to account for historical bias in training data, especially against racial and class minorities. As a result, they run the risk of reinforcing historical prejudice against these already persecuted groups. The aim of team GAHSP is to address these inherent issues with predictive policing while also improving on crime-prediction accuracy. Using modern Machine Learning techniques, better data cleansing/weighting, and algorithm stopgaps such as unfairness penalties, we aim to construct an algorithm which has the benefits of better crime prediction while minimizing bias in ways that past algorithms have not attempted or succeeded at doing.
dc.identifierhttps://doi.org/10.13016/pobg-lfna
dc.identifier.urihttp://hdl.handle.net/1903/32519
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Maryland
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md)
dc.relation.isAvailableAtOffice of Undergraduate Research
dc.subjectComputer Science
dc.subjectCMNS
dc.titleGenerating Algorithms for Hot Spots Policing
dc.typeOther
local.equitableAccessSubmissionNo

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