Trustworthy Machine Learning

Trustworthy Machine Learning
Kush R. Varshney

Accuracy is not enough when you’re developing machine learning systems for consequential application domains. You also need to make sure that your models are fair, have not been tampered with, will not fall apart in different conditions, and can be understood by people. Your design and development process has to be transparent and inclusive. You don’t want the systems you create to be harmful, but to help people flourish in ways they consent to. All of these considerations beyond accuracy that make machine learning safe, responsible, and worthy of our trust have been described by many experts as the biggest challenge of the next five years. I hope this book equips you with the thought process to meet this challenge.

This book is most appropriate for project managers, data scientists, and other practitioners in high-stakes domains who care about the broader impact of their work, have the patience to think about what they’re doing before they jump in, and do not shy away from a little math.

In writing the book, I have taken advantage of the dual nature of my job as an applied data scientist part of the time and a machine learning researcher the other part of the time. Each chapter focuses on a different use case that technologists tend to face when developing algorithms for financial services, health care, workforce management, social change, and other areas. These use cases are fictionalized versions of real engagements I’ve worked on. The contents bring in the latest research from trustworthy machine learning, including some that I’ve personally conducted as a machine learning researcher.

Electronic version available at http://www.trustworthymachinelearning.com.