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Fairness seen as Global Sensitivity Analysis

Abstract : Ensuring that a predictor is not biased against a sensible feature is the key of Fairness learning. Conversely, Global Sensitivity Analysis is used in numerous contexts to monitor the influence of any feature on an output variable. We reconcile these two domains by showing how Fairness can be seen as a special framework of Global Sensitivity Analysis and how various usual indicators are common between these two fields. We also present new Global Sensitivity Analysis indices, as well as rates of convergence, that are useful as fairness proxies.
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Contributor : Clément Bénesse Connect in order to contact the contributor
Submitted on : Friday, March 5, 2021 - 2:21:00 PM
Last modification on : Monday, April 4, 2022 - 3:24:14 PM
Long-term archiving on: : Sunday, June 6, 2021 - 6:57:57 PM


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  • HAL Id : hal-03160697, version 1
  • ARXIV : 2103.04613


Clément Bénesse, Fabrice Gamboa, Jean-Michel Loubes, Thibaut Boissin. Fairness seen as Global Sensitivity Analysis. 2021. ⟨hal-03160697v1⟩



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