Algorithmic fairness with optimal transport: quantifying counterfactual fairness and mitigating group fairness

This Friday, I will be in Laval University, in Québec, to give a talk at the Statlab/INCASS-Québec/CIMMUL day. In this talk, we present two complementary approaches to addressing fairness in algorithmic decision-making, regarding individual and group fairness. First, we use Wasserstein barycenters to obtain (strong Demographic Parity) with one or multiple sensitive features. Our method provides a closed-form solution for the optimal, sequentially fair predictor, enabling possible interpretation of correlations between sensitive attributes. Then, we introduce a novel method that links two …