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 …

Federated learning is an emerging framework that builds centralized machine learning
models with training data distributed across multiple devices. Most of the previous works …

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 …

Fairness-aware learning studies the problem of building machine learning models that are
subject to fairness requirements. Counterfactual fairness is a notion of fairness derived from …

Achieving fairness in learning models is currently an imperative task in machine learning.
Meanwhile, recent research showed that fairness should be studied from the causal …

Machine learning algorithms are used to make decisions in various applications. These
algorithms rely on large amounts of sensitive individual information to work properly. Hence …

The growing use of automated decision-making in critical applications, such as crime
prediction and college admission, has raised questions about fairness in machine learning …

In this paper, we study the fairness-aware classification problem by formulating it as a
constrained optimization problem. Several limitations exist in previous works due to the lack …

Develo** classification methods with high accuracy that also avoid unfair treatment of
different groups has become increasingly important for data-driven decision making in social …

Discrimination discovery and prevention/removal are increasingly important tasks in data
mining. Discrimination discovery aims to unveil discriminatory practices on the protected …