Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

by Yada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll

Genres: Artificial Intelligence

300 pages, Paperback

Published February 7, 2023

Reviews

[Matthew McAteer · Rating 5 out of 5]

This is one of the best AI safety books with code out there......on the grounds of being one of the very few AI safety books with ANY code examples. This book does assume one already knows how to put together a neural network in PyTorch. That being said, it does a pretty good job in being consistent with the use of PyTorch and HuggingFace for the examples.

Disclaimer: I am one of the authors of this book.