Machine learning and access to big data are revolutionizing the way many industries
operate, providing analytics and automation to many aspects of real-world practical tasks …

Bias in machine learning has manifested injustice in several areas, with notable examples
including gender bias in job-related ads [4], racial bias in evaluating names on resumes [3] …

Machine learning algorithms are now frequently used in sensitive contexts that substantially
affect the course of human lives, such as credit lending or criminal justice. This is driven by …

A snapshot of the frontiers of fairness in machine learning Page 1 82 COMMUNICATIONS OF
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …

Fairness is an increasingly important concern as machine learning models are used to
support decision making in high-stakes applications such as mortgage lending, hiring, and …

Machine learning is often used to aid in human decision-making, sometimes for life-altering
decisions like when determining whether or not to grant bail to a defendant or a loan to an …

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 …

Recent advancements in machine learning and deep learning have brought algorithmic
fairness into sharp focus, illuminating concerns over discriminatory decision making that …

Significant progress in the field of fair machine learning (ML) has been made to counteract
algorithmic discrimination against marginalized groups. However, fairness remains an active …

Abstract Machine learning algorithms can now identify patterns and correlations in (big)
datasets and predict outcomes based on the identified patterns and correlations. They can …