A fair classifier using kernel density estimation

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dc.contributor.authorCho, Jaewoongko
dc.contributor.authorSuh, Changhoko
dc.contributor.authorHwang, Gyeongjoko
dc.date.accessioned2020-12-18T07:50:23Z-
dc.date.available2020-12-18T07:50:23Z-
dc.date.created2020-11-28-
dc.date.created2020-11-28-
dc.date.issued2020-12-08-
dc.identifier.citation34th Conference on Neural Information Processing Systems, NeurIPS 2020-
dc.identifier.issn1049-5258-
dc.identifier.urihttp://hdl.handle.net/10203/278708-
dc.description.abstractAs machine learning becomes prevalent in a widening array of sensitive applications such as job hiring and criminal justice, one critical aspect in the design of machine learning classifiers is to ensure fairness: Guaranteeing the irrelevancy of a prediction to sensitive attributes such as gender and race. This work develops a kernel density estimation (KDE) methodology to faithfully respect the fairness constraint while yielding a tractable optimization problem that comes with high accuracy-fairness tradeoff. One key feature of this approach is that the fairness measure quantified based on KDE can be expressed as a differentiable function w.r.t. model parameters, thereby enabling the use of prominent gradient descent to readily solve an interested optimization problem. This work focuses on classification tasks and two well-known measures of group fairness: demographic parity and equalized odds. We empirically show that our algorithm achieves greater or comparable performances against prior fair classifers in accuracy-fairness tradeoff as well as in training stability on both synthetic and benchmark real datasets.-
dc.languageEnglish-
dc.publisherConference on Neural Information Processing Systems-
dc.titleA fair classifier using kernel density estimation-
dc.typeConference-
dc.identifier.wosid000627697000074-
dc.identifier.scopusid2-s2.0-85102543214-
dc.type.rimsCONF-
dc.citation.publicationname34th Conference on Neural Information Processing Systems, NeurIPS 2020-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorSuh, Changho-
dc.contributor.nonIdAuthorCho, Jaewoong-
dc.contributor.nonIdAuthorHwang, Gyeongjo-
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EE-Conference Papers(학술회의논문)
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