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 …

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Post hoc auditing of model fairness suffers from potential drawbacks:(1) auditing may be
highly sensitive to the test samples chosen;(2) the model and/or its training data may need to …

The increasing use of machine learning in high-stakes domains–where people's livelihoods
are impacted–creates an urgent need for interpretable, fair, and highly accurate algorithms …

Recent years have seen the rapid development of fairness-aware machine learning in
mitigating unfairness or discrimination in decision-making in a wide range of applications …

The use of algorithmic decision-making systems based on machine learning models has led
to a need for fair (unbiased) and explainable classification outcomes. In particular, machine …

In recent years, artificial intelligence technology has been widely used in many fields, such
as computer vision, natural language processing and autonomous driving. Machine learning …

Fairness has been a critical issue that affects the adoption of deep learning models in real
practice. To improve model fairness, many existing methods have been proposed and …

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 …