DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Jaewoong | ko |
dc.contributor.author | Suh, Changho | ko |
dc.contributor.author | Hwang, Gyeongjo | ko |
dc.date.accessioned | 2020-12-18T07:50:23Z | - |
dc.date.available | 2020-12-18T07:50:23Z | - |
dc.date.created | 2020-11-28 | - |
dc.date.created | 2020-11-28 | - |
dc.date.issued | 2020-12-08 | - |
dc.identifier.citation | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.identifier.issn | 1049-5258 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278708 | - |
dc.description.abstract | As 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.language | English | - |
dc.publisher | Conference on Neural Information Processing Systems | - |
dc.title | A fair classifier using kernel density estimation | - |
dc.type | Conference | - |
dc.identifier.wosid | 000627697000074 | - |
dc.identifier.scopusid | 2-s2.0-85102543214 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Suh, Changho | - |
dc.contributor.nonIdAuthor | Cho, Jaewoong | - |
dc.contributor.nonIdAuthor | Hwang, Gyeongjo | - |
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