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 …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

Datasets often contain biases which unfairly disadvantage certain groups, and classifiers
trained on such datasets can inherit these biases. In this paper, we provide a mathematical …

Training classifiers under fairness constraints such as group fairness, regularizes the
disparities of predictions between the groups. Nevertheless, even though the constraints are …

Abstract 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 …

The use of machine learning to guide clinical decision making has the potential to worsen
existing health disparities. Several recent works frame the problem as that of algorithmic …

We show that many machine learning goals can be expressed as “rate constraints” on a
model's predictions. We study the problem of training non-convex models subject to these …

We study the problem of learning fair prediction models for unseen test sets distributed
differently from the train set. Stability against changes in data distribution is an important …

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 …

In recent years, constrained optimization has become increasingly relevant to the machine
learning community, with applications including Neyman-Pearson classification, robust …