Practical Fairness

Preface

Welcome to Practical Fairness. I wrote this book because data scientists and machine learning engineers are increasingly aware of the fairness implications of their work but are not adequately empowered to do anything about their concerns. Academic research on mathematical solutions to fairness concerns is flourishing, and myriad open source options are available thanks to both academic researchers and technology companies sharing resources. However, delving into the topic in a practical and concrete way remains difficult for the beginner, and best practices have not yet emerged in most industries to address even the most basic concerns. This book’s aim is to be an accessible overview for beginners in this field with actionable fairness advice.

Goals of This Book

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 doesn’t have a one-size-fits-all solution, but you should be able to:

• Identify potential fairness problems in your organization’s 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 ...