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- handle: 10670/1.ehp277
- halshs: halshs-03500906
info:eu-repo/semantics/OpenAccess
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fairness nonparametric regression classification accuracy C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C10 - General C - Mathematical and Quantitative Methods/C.C3 - Multiple or Simultaneous Equation Models • Multiple Variables/C.C3.C38 - Classification Methods • Cluster Analysis • Principal Components • Factor Models C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation MethodsSujets proches
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Jean-David Fermanian et al., « Fair learning with bagging », HAL-SHS : économie et finance, ID : 10670/1.ehp277
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Résumé
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.