303 citations · 438 across the 5 of their papers we have counts for
3 papers · 1 filter
Learning Fair and Transferable Representations
Luca Oneto, Michele Donini, Andreas Maurer +1
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data rep…
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +2
We study the problem of fair binary classification using the notion of Equal Opportunity. It requires the true positive rate to distribute equally across the sensitive groups. With…
General Fair Empirical Risk Minimization
Luca Oneto, Michele Donini, Massimiliano Pontil
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible contin…