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 …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

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 …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

We propose an approach to fair classification that enforces independence between the
classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The …

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 …

In this work, we investigate whether privacy and fairness can be simultaneously achieved by
a single classifier in several different models. Some of the earliest work on fairness in …

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 …

In domains such as medicine, it can be acceptable for machine learning models to include
sensitive attributes such as gender and ethnicity. In this work, we argue that when there is …