Includes index.

Preface -- Privacy -- Fairness and bias -- Model explainability and interpretability -- Robustness -- Secure and trustworthy data generation -- More state-of-the-art research questions -- From theory to practice -- An ecosystem of trust -- Synthetic data generation tools -- Other interpretability and explainability tool kits -- Index -- About the authors

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. -- Provided by publisher.