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 …

Abstract Machine learning is becoming increasingly prominent in healthcare. Although its
benefits are clear, growing attention is being given to how these tools may exacerbate …

The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic
critical decision support software systems. DNNs are exceptionally good at finding min-imal …

Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …

Machine Learning (ML) software has been widely adopted in modern society, with reported
fairness implications for minority groups based on race, sex, age, etc. Many recent works …

The increasing use of Machine Learning (ML) software can lead to unfair and unethical
decisions, thus fairness bugs in software are becoming a growing concern. Addressing …

Abstract Machine learning is part of the daily life of people and companies worldwide.
Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision …

Fairness is a crucial non-functional requirement of modern software systems that rely on the
use of Artificial Intelligence (AI) to make decisions regarding our daily lives in application …

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 …