A recent wave of research has attempted to define fairness quantitatively. In particular, this
work has explored what fairness might mean in the context of decisions based on the …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

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 …

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 …

In recent years, the problem of addressing fairness in machine learning (ML) and automatic
decision making has attracted a lot of attention in the scientific communities dealing with …

A growing body of work shows that many problems in fairness, accountability, transparency,
and ethics in machine learning systems are rooted in decisions surrounding the data …

As machine learning increasingly affects people and society, it is important that we strive for
a comprehensive and unified understanding of how and why unwanted consequences …

The machine learning community has become increasingly concerned with the potential for
bias and discrimination in predictive models. This has motivated a growing line of work on …

The last few years have seen an explosion of academic and popular interest in algorithmic
fairness. Despite this interest and the volume and velocity of work that has been produced …