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dc.contributor.authorKing, Jennifer
dc.contributor.authorHo, Daniel
dc.contributor.authorGupta, Arushi
dc.contributor.authorWu, Victor
dc.contributor.authorWebley-Brown, Helen
dc.date.accessioned2023-07-10T15:47:38Z
dc.date.available2023-07-10T15:47:38Z
dc.date.issued2023-06-12
dc.identifier.isbn979-8-4007-0192-4
dc.identifier.urihttps://hdl.handle.net/1721.1/151052
dc.publisherACM|2023 ACM Conference on Fairness, Accountability, and Transparencyen_US
dc.relation.isversionofhttps://doi.org/10.1145/3593013.3594015en_US
dc.rightsCreative Commons Attribution-Noncommercial-NoDerivativesen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleThe Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Governmenten_US
dc.typeArticleen_US
dc.identifier.citationKing, Jennifer, Ho, Daniel, Gupta, Arushi, Wu, Victor and Webley-Brown, Helen. 2023. "The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Political Science
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-07-01T08:00:57Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-07-01T08:00:57Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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