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 …

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 …

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 …

The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

In the current era, people and society have grown increasingly reliant on artificial
intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of …

In this paper, we study the prediction of a real-valued target, such as a risk score or
recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a …

Algorithmic decision making systems are ubiquitous across a wide variety of online as well
as offline services. These systems rely on complex learning methods and vast amounts of …

We introduce a flexible family of fairness regularizers for (linear and logistic) regression
problems. These regularizers all enjoy convexity, permitting fast optimization, and they span …

Society is increasingly relying on data-driven predictive models for automated decision
making. This is not by design, but due to the nature and noisiness of observational data …