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dc.contributor.authorBalagopalan, Aparna
dc.contributor.authorZhang, Haoran
dc.contributor.authorHamidieh, Kimia
dc.contributor.authorHartvigsen, Thomas
dc.contributor.authorRudzicz, Frank
dc.contributor.authorGhassemi, Marzyeh
dc.date.accessioned2022-11-03T17:39:47Z
dc.date.available2022-11-03T17:39:47Z
dc.date.issued2022-06-21
dc.identifier.isbn978-1-4503-9352-2
dc.identifier.urihttps://hdl.handle.net/1721.1/146127
dc.publisherACM|2022 ACM Conference on Fairness, Accountability, and Transparencyen_US
dc.relation.isversionofhttps://doi.org/10.1145/3531146.3533179en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACM|2022 ACM Conference on Fairness, Accountability, and Transparencyen_US
dc.titleThe Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationsen_US
dc.typeArticleen_US
dc.identifier.citationBalagopalan, Aparna, Zhang, Haoran, Hamidieh, Kimia, Hartvigsen, Thomas, Rudzicz, Frank et al. 2022. "The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer 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.updated2022-11-03T07:45:10Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-03T07:45:10Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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