34 citations · 171 across the 39 of their papers we have counts for
3 papers · 1 filter
Counterfactually Fair Regression via Optimal Transport
M. Generali Lince, S. Gaucher, J-J. Vie +1
We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus…
Bounding and Approximating Intersectional Fairness through Marginal Fairness
Mathieu Molina, Patrick Loiseau
Discrimination in machine learning often arises along multiple dimensions (a.k.a. protected attributes); it is then desirable to ensure \emph{intersectional fairness} -- i.e., that…
The Price of Local Fairness in Multistage Selection
Vitalii Emelianov, George Arvanitakis, Nicolas Gast +2
The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applicat…