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 …

External audits of AI systems are increasingly recognized as a key mechanism for AI
governance. The effectiveness of an audit, however, depends on the degree of access …

With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …

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

Code completion, a highly valuable topic in the software development domain, has been
increasingly promoted for use by recent advances in large language models (LLMs). To …

The widespread adoption of Machine Learning systems, especially in more decision-critical
applications such as criminal sentencing and bank loans, has led to increased concerns …

Machine learning (ML) systems have achieved remarkable performance across a wide area
of applications. However, they frequently exhibit unfair behaviors in sensitive application …

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 …

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 …