The problem of fair machine learning has drawn much attention over the last few years and
the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Machine learning has begun to automate decisions that profoundly affect individuals, raising
concerns about the technology's effectiveness, fairness, and the extent to which it obfuscates …

Over the past several years, a multitude of methods to measure the fairness of a machine
learning model have been proposed. However, despite the growing number of publications …

As automated data analysis supplements, and even replaces, human supervision in
decision making, there are growing societal concerns about potential unfairness of these …

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 …

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 …

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 …

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] …