As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

We study fair machine learning (ML) under predictive uncertainty to enable reliable and
trustworthy decision-making. The seminal work of'equalized coverage'proposed an …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

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 …

In the context of regression, we consider the fundamental question of making an estimator
fair while preserving its prediction accuracy as much as possible. To that end, we define its …

Selective regression allows abstention from prediction if the confidence to make an accurate
prediction is not sufficient. In general, by allowing a reject option, one expects the …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …

Algorithmic Fairness is an established field in machine learning that aims to reduce biases
in data. Recent advances have proposed various methods to ensure fairness in a univariate …

Classification with abstention has gained a lot of attention in recent years as it allows to
incorporate human decision-makers in the process. Yet, abstention can potentially amplify …