Hello and welcome! I’m very happy that you’ve entrusted me and the book Trustworthy Machine Learning to accompany you on your journey toward creating trustworthy machine learning systems. I am making the book available at no cost because I do not want to limit its contents only to the most resourced. The paperback version is available on Amazon for $6.85, the lowest price possible to cover the printing costs.
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 technologists 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 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 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.
—Kush
Trustworthy Machine Learning (the entire book) pdf paperback slides
Front Matter and Preface pdf html
Part 1: Introduction and Preliminaries
Chapter 1: Establishing Trust pdf html
Chapter 2: Machine Learning Lifecycle pdf html
Chapter 3: Safety pdf html
Part 2: Data
Chapter 4: Data Modalities, Sources, and Biases pdf html
Chapter 5: Privacy and Consent pdf html
Part 3: Basic Modeling
Chapter 6: Detection Theory pdf html
Chapter 7: Supervised Learning pdf html
Chapter 8: Causal Modeling pdf html
Part 4: Reliability
Chapter 9: Distribution Shift pdf html
Chapter 10: Fairness pdf html
Chapter 11: Adversarial Robustness pdf html
Part 5: Interaction
Chapter 12: Interpretability and Explainability pdf html
Chapter 13: Transparency pdf html
Chapter 14: Value Alignment pdf html
Part 6: Purpose
Chapter 15: Ethics Principles pdf html
Chapter 16: Lived Experience pdf html
Chapter 17: Social Good pdf html
Chapter 18: Filter Bubbles and Disinformation pdf html
Author Contact Information
Email: krvarshn@us.ibm.com
Twitter: @krvarshney
Telephone: +1-914-945-1628