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dc.contributor.authorWawira Gichoya, Judy
dc.contributor.authorMcCoy, Liam G
dc.contributor.authorCeli, Leo Anthony G.
dc.contributor.authorGhassemi, Marzyeh
dc.date.accessioned2021-09-27T17:00:49Z
dc.date.available2021-09-27T17:00:49Z
dc.date.issued2021-04
dc.date.submitted2020-11
dc.identifier.issn2632-1009
dc.identifier.urihttps://hdl.handle.net/1721.1/132648
dc.description.sponsorshipNational Institute of Biomedical Imaging and Bioengineering (Grants EB017205 and 1928481)en_US
dc.publisherBMJen_US
dc.relation.isversionofhttp://dx.doi.org/10.1136/bmjhci-2020-100289en_US
dc.rightsCreative Commons Attribution NonCommercial License 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceBMJ Health and Care Informaticsen_US
dc.titleEquity in essence: a call for operationalising fairness in machine learning for healthcareen_US
dc.typeArticleen_US
dc.identifier.citationWawira Gichoya, Judy et al. "Equity in essence: a call for operationalising fairness in machine learning for healthcare." BMJ Health and Care Informatics 28, 1 (April 2021): e100289. ©2021 The Author(s)en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.relation.journalBMJ Health and Care Informaticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2021-05-25T17:25:38Z
mit.journal.volume28en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusCompleteen_US


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