Trustworthy Machine Learning

by Kush R. Varshney

Hello and welcome! Iím very happy that youíve entrusted me and the book Trustworthy Machine Learning to accompany you on your journey toward creating trustworthy machine learning systems. The book is still under development, and I will be posting additional chapters as I write them. I am making the book available at no cost because I do not want to limit its contents only to the most resourced.

Accuracy is not enough when youíre 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 donít 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 5 years. I hope this book equips you with the thought process to meet this challenge.

This book is most appropriate for technologists in high-stakes domains who care about the broader impact of their work, have the patience to think about what theyíre 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 day-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 project managers, data scientists, and other practitioners tend to face when developing algorithms for financial services, healthcare, workforce management, social change, and other areas. These use cases are fictionalized versions of real engagements Iíve worked on. The contents bring in the latest research from the fast-moving field of trustworthy machine learning, including some that Iíve personally conducted as a machine learning researcher.

I urge, urge, urge you to engage with me and critique the content, especially if you have lived experiences different from mine. I truly want to reflect the opinions of diverse voices in this work. Please contact me by email ( or on Twitter (@krvarshney).

Thanks again and happy reading.


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