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Fair learning with bagging

Author

Listed:
  • Jean-David Fermanian

    (ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique)

  • 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])

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.

Suggested Citation

  • Jean-David Fermanian & Dominique Guegan, 2021. "Fair learning with bagging," Post-Print halshs-03500906, HAL.
  • Handle: RePEc:hal:journl:halshs-03500906
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03500906
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    References listed on IDEAS

    as
    1. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
    2. Anderson, N. H. & Hall, P. & Titterington, D. M., 1994. "Two-Sample Test Statistics for Measuring Discrepancies Between Two Multivariate Probability Density Functions Using Kernel-Based Density Estimates," Journal of Multivariate Analysis, Elsevier, vol. 50(1), pages 41-54, July.
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    Keywords

    fairness; nonparametric regression; classification; accuracy;
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