Practicing Trustworthy Machine Learning

Preface

We live in a world where machine learning (ML) systems are used in increasingly high-stakes domains like medicine, law, and defense. Model decisions can result in economic gains or losses in the millions or billions of dollars. Because of the high-stakes nature of their decisions and consequences, it is important for these ML systems to be trustworthy. This can be a problem when the ML systems are not secure, may fail unpredictably, have notable performance disparities across sample groups, and/or struggle to explain their decisions. We wrote this book to help your ML models stand up on their own in the real world.

Implementing Machine Learning in Production

If you’re reading this book, you are probably already aware of the incredibly outsized importance of ML. Regardless of the fields of application, ML techniques touch all of our lives. Google Brain cofounder Andrew Ng was not exaggerating when he described AI as “the new electricity”. After all, what we have on our hands could best be described as a universal function approximator. Much like electricity, ML can be dangerous if not handled properly. Like a discharge from a high-voltage wire colliding with a mylar balloon, cases of ML failure can be unexpected and scary.

Deploying ML applications in the real world is quite different from working on models in closed environments. Academic datasets often do not carry the full variation of real-world data. Data that our models interact with in the future may not resemble ...