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 …

Deep neural networks (DNNs) have made significant progress, but often suffer from fairness
issues, as deep models typically show distinct accuracy differences among certain …

Abstract Fairness issues in Deep Learning models have recently received increasing
attention due to their significant societal impact. Although methods for mitigating unfairness …

Deep Neural Networks (DNNs), an emerging software technology, have achieved
impressive results in a variety of fields. However, the discriminatory behaviors towards …

The rapid development of AI technologies has found numerous applications across various
domains in human society. Ensuring fairness and preventing discrimination are critical …

Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory
manner, with respect to protected attributes such as gender, race, or age. Ensuring fairness …

The issue of bias in Machine Learning (ML) models is a significant challenge for the
machine learning community. Real-world biases can be embedded in the data used to train …