Fair learning with bagging

Jean-David Fermanian () and Dominique Guegan ()
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Jean-David Fermanian: ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - IP Paris - Institut Polytechnique de Paris
Dominique Guegan: UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy]

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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)
Date: 2021-11
New Economics Papers: this item is included in nep-big
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03500906v1
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Published in 2021

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-03500906

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