Fair learning with bagging

Jean-David Fermanian () and Dominique Guégan ()
Additional contact information
Jean-David Fermanian: Ensae-Crest, https://www.ensae.fr/
Dominique Guégan: Université Paris1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, - Ca' Foscari University of Venezia, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: The central question of this paper is how to enhance supervised learning algorithms with fairness requirement ensuring that any sensitive input does not "unfairly"' influence the outcome of the learning algorithm. To attain this objective we proceed by three steps. First after introducing several notions of fairness in a uniform approach, we introduce a more general notion through conditional fairness definition which englobes most of the well known fairness definitions. Second we use a ensemble of binary and continuous classifiers to get an optimal solution for a fair predictive outcome using a related-post-processing procedure without any transformation on the data, nor on the training algorithms. Finally we introduce several tests to verify the fairness of the predictions. Some empirics are provided to illustrate our approach

Keywords: fairness; nonparametric regression; classification; accuracy (search for similar items in EconPapers)
JEL-codes: C10 C38 C53 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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http://mse.univ-paris1.fr/pub/mse/CES2021/21034.pdf (application/pdf)
https://halshs.archives-ouvertes.fr/halshs-03500906

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:21034

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