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 …

Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

Existing research mostly improves the fairness of Machine Learning (ML) software regarding
a single protected attribute at a time, but this is unrealistic given that many users have …

Decision-making software mainly based on Machine Learning (ML) may contain fairness
issues (eg, providing favourable treatment to certain people rather than others based on …

This paper conducts fairness testing of automated pedestrian detection, a crucial but under-
explored issue in autonomous driving systems. We evaluate eight state-of-the-art deep …

Data bias is a major concern in biomedical research, especially when evaluating large-scale
observational datasets. It leads to imprecise predictions and inconsistent estimates in …

Most works on the fairness of machine learning systems focus on the blind optimization of
common fairness metrics, such as Demographic Parity and Equalized Odds. In this paper …

There has been an increasing interest in enhancing the fairness of machine learning (ML).
Despite the growing number of fairness-improving methods, we lack a systematic …

This paper conducts fairness testing on automated pedestrian detection, a crucial but under-
explored issue in autonomous driving systems. We evaluate eight widely-studied pedestrian …