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dc.contributor.authorGervasi, Stephanie S
dc.contributor.authorChen, Irene Y
dc.contributor.authorSmith-McLallen, Aaron
dc.contributor.authorSontag, David
dc.contributor.authorObermeyer, Ziad
dc.contributor.authorVennera, Michael
dc.contributor.authorChawla, Ravi
dc.date.accessioned2022-07-20T16:38:14Z
dc.date.available2022-07-20T16:38:14Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/143894
dc.language.isoen
dc.publisherHealth Affairs (Project Hope)en_US
dc.relation.isversionof10.1377/HLTHAFF.2021.01287en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceHealth Affairsen_US
dc.titleThe Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.en_US
dc.typeArticleen_US
dc.identifier.citationGervasi, Stephanie S, Chen, Irene Y, Smith-McLallen, Aaron, Sontag, David, Obermeyer, Ziad et al. 2022. "The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.." Health Affairs, 41 (2).
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalHealth Affairsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-20T16:22:57Z
dspace.orderedauthorsGervasi, SS; Chen, IY; Smith-McLallen, A; Sontag, D; Obermeyer, Z; Vennera, M; Chawla, Ren_US
dspace.date.submission2022-07-20T16:22:58Z
mit.journal.volume41en_US
mit.journal.issue2en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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