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 …

Transfer learning, successful in knowledge translation across related tasks, faces a
substantial privacy threat from membership inference attacks (MIAs). These attacks, despite …

As smartphones proliferate, secure and user-friendly authentication methods are
increasingly critical. Existing behavioral biometrics, however, are often compromised by …

Background: The use of social media for disseminating health care information has become
increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine …

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 …

Machine translation is integral to international communication and extensively employed in
diverse human-related applications. Despite remarkable progress, fairness issues persist …

Machine learning (ML) is increasingly being used in critical decision-making software, but
incidents have raised questions about the fairness of ML predictions. To address this issue …

This paper investigates neuron dropout as a post-processing bias mitigation method for
deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in …

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 …