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
dc.description.urihttp://jfoulds.informationsystems.umbc.edu/slides/2018/Foulds_Nov_14_2018_MD_AI_Differential_Fairness.pdfen
dc.format.extent47 pagesen
dc.genreconference papers and proceedingsen
dc.identifierdoi:10.13016/m2niie-ahvn
dc.identifier.urihttp://hdl.handle.net/11603/12621
dc.language.isoenen
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
dc.subjectartificial intelligenceen
dc.subjectclassificationen
dc.subjectdataen
dc.subjectintersectionalityen
dc.subjectinfra-marginalityen
dc.titleDifferential Fairness for Machine Learning and Artificial Intelligence Systems: Unbiased Decisions with Biased Dataen
dc.typeTexten

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