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dc.contributor.authorFoulds, James
dc.contributor.authorPan, Shimei
dc.date.accessioned2018-09-05T17:10:14Z
dc.date.available2018-09-05T17:10:14Z
dc.date.issued2018-07
dc.description.abstractWe introduce a measure of fairness for algorithms and data with regard to multiple protected attributes. Our proposed definition, differential fairness, is informed by the framework of intersectionality, which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we illustrate links to differential privacy. A case study on census data demonstrates the utility of our approach.en_US
dc.description.urihttps://arxiv.org/abs/1807.08362en_US
dc.format.extent16 PAGESen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/M2PV6BB0C
dc.identifier.uri2018arXiv180708362F
dc.identifier.urihttp://hdl.handle.net/11603/11227
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectComputer Science - Machine Learningen_US
dc.subjectComputer Science - Computers and Societyen_US
dc.subjectStatistics - Machine Learningen_US
dc.subjectmeasure of fairness
dc.subjectalgorithms and data
dc.subjectprotected attributes
dc.subjectsystems of power
dc.subjectsystems of oppression
dc.subjectrace
dc.subjectgender
dc.subjectsexual orientation
dc.subjectclass
dc.subjectdisability
dc.titleAn Intersectional Definition of Fairnessen_US
dc.typeTexten_US


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