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 …

With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

We consider the problem of learning fair decision systems from data in which a sensitive
attribute might affect the decision along both fair and unfair pathways. We introduce a …

Abstract Machine learning models have been criticized for reflecting unfair biases in the
training data. Instead of solving for this by introducing fair learning algorithms directly, we …

Fairness-aware learning is increasingly important in data mining. Discrimination prevention
aims to prevent discrimination in the training data before it is used to conduct predictive …