As machine learning (ML) is deployed in high-stakes domains, such as disease diagnosis or
prison sentencing, questions of fairness have become an area of concern in its …

The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …

A distinction has been drawn in fair machine learning research between'group'and'
individual'fairness measures. Many technical research papers assume that both are …

Recent research has shown that seemingly fair machine learning models, when used to
inform decisions that have an impact on peoples' lives or well-being (eg, applications …

Machine learning algorithms that aid human decision-making may inadvertently discriminate
against certain protected groups. Therefore, we formalize direct discrimination as a direct …

Both industry and academia have made considerable progress in developing trustworthy
and responsible machine learning (ML) systems. While critical concepts like fairness and …

Fairness in Artificial Intelligence rightfully receives a lot of attention these days. Many life-
impacting decisions are being partially automated, including health-care resource planning …

The importance of incorporating ethics and legal compliance into machine-assisted decision-
making is broadly recognized. Further, several lines of recent work have argued that critical …

Machine learning algorithms are increasingly used to make or support important decisions
about people's lives. This has led to interest in the problem of fair classification, which …

While artificial intelligence (AI) and machine learning (ML) have been increasingly used for
decision-making, issues related to discrimination in AI/ML have become prominent. While …