Aileen Nielsen - Practical Fairness

• 2020-12-01: First Release

This book will help practicing data scientists and technologists get their feet wet in the world of fairness. The goal is that after reading this book, you can actively pursue fairness in your own work. Fairness doesnt have a one-size-fits-all solution, but you should be able to:

• Identify potential fairness problems in your organizations workflow and data practices
• Break fairness concerns into conceptually distinct categories and target appropriate areas of research literature accordingly
• Offer accessible guidance to nontechnical colleagues on potential fairness problems and solutions in whatever machine learning products you are developing

In this book I exclusively use Python examples and focus on the easiest interfaces available via open source options for implementing relevant fairness methods. I chose this approach because Python benefits from a large degree of open source work in the fairness domain. However, a good deal of work in other languages unfortunately had to be ignored, including in Java, R, and MATLAB code. Also, code bases with organizational sponsors and with a larger breadth of tools were favored over smaller code bases or code bases maintained by just a few individuals. This also meant that some very interesting and high-quality work was omitted. You should be aware that the goal of this book is far more to help you get conceptually organized and that the packages highlighted here are just one set of tools, not necessarily the best or only tools. The fairness toolbox continues to grow rapidly, and there is every reason to expect more tools and code bases to develop over time. The selections in this book represent just one snapshot of convenient APIs.

Fairness in machine learning and in the technology sector remains an active struggle, an ongoing social concern, and an interesting engineering problem. We need legal, economic, and social solutions as well as technical ones. Toward that end, 50% of the royalties earned on this book will be donated to the American Civil Liberties Union, an organization that has relentlessly pursued fairness for a hundred years. The ACLU is actively working to secure fundamental rights to privacy and fairness in the era of algorithms. Readers should be aware that the ACLU played no part in the writing of this book, has not reviewed it, and has not endorsed it. Nevertheless my hope is that this book will reinforce and support the ACLUs important work in more ways than one.

The other 50% of royalties earned on this book will be donated to Mercy for Animals, an organization dedicated to radically expanding our definitions of fairness to include the well-being of animals and Earth. Mercy for Animals takes a practical approach to addressing devastating cruelty and unfairness practiced in the fundamental industries that bring food to our table. Thus, my hope is that this books royalties can contribute to our societys ongoing development of a fairer and more inclusive outlook.