Fairness and Machine Learning

Barocas, Solon; Hardt, Moritz; Narayanan, Arvind

This book gives a perspective on machine learning that treats fairness as a central concern rather than an afterthought. We’ll review the practice of machine learning in a way that highlights ethical challenges. We’ll then discuss approaches to mitigate these problems.

We’ve aimed to make the book as broadly accessible as we could, while preserving technical rigor and confronting difficult moral questions that arise in algorithmic decision making.

This book won’t have an all-encompassing formal definition of fairness or a quick technical fix to society’s concerns with automated decisions. Addressing issues of fairness requires carefully understanding the scope and limitations of machine learning tools. This book offers a critical take on current practice of machine learning as well as proposed technical fixes for achieving fairness. It doesn’t offer any easy answers. Nonetheless, we hope you’ll find the book enjoyable and useful in developing a deeper understanding of how to practice machine learning responsibly.