Differential Fairness for Machine Learning and Artificial Intelligence Systems: Unbiased Decisions with Biased Data

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dc.contributor.authorFoulds, James
dc.contributor.authorIslam, Rashidul
dc.contributor.authorKeya, Kamrun
dc.contributor.authorPan, Shimei
dc.date.accessioned2019-01-28T15:32:22Z
dc.date.available2019-01-28T15:32:22Z
dc.date.issued2018-11-14
dc.descriptionArtificial Intelligence Maryland Meetup, Emerging Technology Centersen_US
dc.description.urihttp://jfoulds.informationsystems.umbc.edu/slides/2018/Foulds_Nov_14_2018_MD_AI_Differential_Fairness.pdfen_US
dc.format.extent47 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2niie-ahvn
dc.identifier.urihttp://hdl.handle.net/11603/12621
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.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmachine learningen_US
dc.subjectartificial intelligenceen_US
dc.subjectclassificationen_US
dc.subjectdataen_US
dc.subjectintersectionalityen_US
dc.subjectinfra-marginalityen_US
dc.titleDifferential Fairness for Machine Learning and Artificial Intelligence Systems: Unbiased Decisions with Biased Dataen_US
dc.typeTexten_US

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