Past literature has been effective in demonstrating ideological gaps in machine learning
(ML) fairness definitions when considering their use in complex socio-technical systems …

This survey article assesses and compares existing critiques of current fairness-enhancing
technical interventions in machine learning (ML) that draw from a range of non-computing …

In recent years fairness in machine learning (ML) has emerged as a highly active area of
research and development. Most define fairness in simple terms, where fairness means …

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

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 …

Machine learning (ML) fairness research tends to focus primarily on mathematically-based
interventions on often opaque algorithms or models and/or their immediate inputs and …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

There is growing concern that decision-making informed by machine learning (ML)
algorithms may unfairly discriminate based on personal demographic attributes, such as …

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 …

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring,
and criminal justice. In response, computer scientists have developed myriad definitions of …