Equity in learning problems: an OWA approach

Juliette Ortholand (1) , Sébastien Destercke (1) , Khaled Belahcene (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

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.

Domaines: Intelligence artificielle [cs.AI]

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⟩