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 …

Dynamic learning systems subject to selective labeling exhibit censoring, ie persistent
negative predictions assigned to one or more subgroups of points. In applications like …

Neglecting the effect that decisions have on individuals (and thus, on the underlying data
distribution) when designing algorithmic decision-making policies may increase inequalities …

Algorithms are increasingly used to automate large-scale decision-making processes, eg,
online platforms that make instant decisions in lending, hiring, and education. When such …

As automated decision making and decision assistance systems become common in
everyday life, research on the prevention or mitigation of potential harms that arise from …

We examine machine learning models in a setup where individuals have the choice to share
optional personal information with a decision-making system, as seen in modern insurance …

In many predictive contexts (eg, credit lending), true outcomes are only observed for
samples that were positively classified in the past. These past observations, in turn, form …

Missing values in real-world data pose a significant and unique challenge to algorithmic
fairness. Different demographic groups may be unequally affected by missing data, and …

Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory
manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness …

In today's world, where humans heavily rely on intelligent systems for everyday decisions,
data and algorithmic biases have become a critical concern. From trivial cases like TV show …