303 citations · 438 across the 5 of their papers we have counts for
7 papers
Fairness in Machine Learning
Luca Oneto, Silvia Chiappa
Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that ma…
Fair Regression with Wasserstein Barycenters
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +2
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of t…
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…
Taking Advantage of Multitask Learning for Fair Classification
Luca Oneto, Michele Donini, Amon Elders +1
A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information…