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 …

As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

Increasingly, software is making autonomous decisions in case of criminal sentencing,
approving credit cards, hiring employees, and so on. Some of these decisions show bias …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

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

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 …

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

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …