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Equity in learning problems: an OWA approach

Abstract : It is well-known in computational social choice that the weighted average does not guarantee any equity or fairness in the share of goods. In a supervised learning problem, this translates into the fact that the empirical risk will lead to models that are good in average, but may have terrible performances for under-represented populations. Such a behaviour is quite damaging in some problems, such as the ones involving imbalanced data sets, in the inputs or the outputs (default prediction, ethical issues,. . .). On the other hand, the OWA operator is known in computational social choice to be able to correct this unfairness. This paper proposes a means to transpose this feature to the supervised learning setting.
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Contributor : Sébastien Destercke Connect in order to contact the contributor
Submitted on : Monday, September 21, 2020 - 5:41:49 PM
Last modification on : Tuesday, November 16, 2021 - 4:29:29 AM
Long-term archiving on: : Thursday, December 3, 2020 - 3:24:33 PM


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Juliette Ortholand, Sébastien Destercke, Khaled Belahcene. Equity in learning problems: an OWA approach. 14th International Conference on Scalable Uncertainty Management (SUM 2020), Jul 2020, Bolzano, Italy. pp.187-199, ⟨10.1007/978-3-030-58449-8_13⟩. ⟨hal-02944888⟩



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