Séminaire MODAL'X : Christophe Denis (LPSM)

Résumé : Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data.Yet, despite its wide range of applications, very few works consider the multi-class classification setting from the fairness perspective.In this talk, we focus on this question and extend the definition of approximate fairness in the case of Demographic Parity to multi-class classification.We specify the corresponding expressions of the optimal fair classifiers and describe a post-processing approach based on the plug-in principle to estimate the optimal predictor. We also illustrate the performance on simulated data.