Patrick Hall - Machine Learning for High-Risk Applications: Approaches to Responsible AI

Machine Learning for High-Risk Applications is a practical, opinionated, and timely book. Readers of all stripes will find rich insights into this fraught subject, whether youre a data scientist interested in better understanding your models, or a manager responsible for ensuring compliance with existing standards, or an executive trying to improve your organizations risk controls.

Agus Sudjianto, PhD, EVP, Head of Corporate Model Risk, Wells Fargo

Dont miss out on this must-read! Packed with a winning combination of cutting-edge theory and real-world expertise, this book is a game-changer for anyone grappling with the complexities of AI interpretability, explainability, and security. With expert guidance on managing bias and much more, its the ultimate guide to mastering the buzzword bonanza of the AI world. Dont let the competition get aheadget your hands on this indispensable resource today!

Mateusz Dymczyk, Software Engineer, Machine Learning, Meta

The book is a comprehensive and timely guide for anyone working on machine learning when the stakes are high. The authors have done an excellent job providing an overview of regulatory aspects, risk management, interpretability, and many other topics while providing practical advice and code examples. Highly recommended for anyone who prefers diligence over disaster when deploying machine learning models.

Christoph Molnar, Author of Interpretable Machine Learning

Machine learning applications need to account for fairness, accountability, transparency, and ethics in every industry to be successful. Machine Learning for High-Risk Applications lays the foundation for such topics and gives valuable insights that can be utilized for various use cases. I highly recommend this book for any machine learning practitioners.

Navdeep Gill, Engineering Manager, H2O.ai

Responsible AIexplained simply.

Hariom Tatsat, Coauthor of Machine Learning & Data Science Blueprints for Finance

Machine Learning for High-Risk Applications is a highly needed book responding to the growing demand for in-depth analysis of predictive models. The book is very practical and gives explicit advice on how to look at different aspects, such as model debugging, bias, transparency, and explainability analysis. The authors share their huge experience in analyzing different classes of models, for both tabular and image data. I recommend this book to anyone wishing to work responsibly with complex models, not only in high-risk applications.

Przemysaw Biecek, Professor at the Warsaw University of Technology

A refreshingly thoughtful and practical guide to responsible use of machine learning. This book has the potential to prevent AI accidents and harms before they happen.

Harsh Singhal, Senior AI Solution Director, Financial Services, C3.ai

This book stands out for its uniquely tactical approach to addressing system risks in ML. The authors emphasize the critical importance of addressing potential harms as necessary to the delivery of desired outcomesnoted as key to the very success of ML. Especially helpful is the focus on ensuring that the right roles are in the room when making decisions about ML. By taking a nuanced approach to derisking ML, this book offers readers a valuable resource for successfully deploying ML systems in a responsible and sustainable manner.

Liz Grennan, Associate Partner and Global Co-Lead for Digital Trust, McKinsey & Company

This book is a comprehensive review of both social and technical approaches to high-risk AI applications and provides practitioners with useful techniques to bridge their day-to-day work with core concepts in Responsible AI.

Triveni Gandhi, PhD, Responsible AI Lead, Dataiku

Unlocking the full potential of machine learning and AI goes beyond mere accuracy of models. This book delves into the critical yet often overlooked aspects of explainable, bias-free, and robust models. In addition, it offers invaluable insights into the cultural and organizational best practices for organizations to ensure the success of their AI initiatives. With technology advancing at an unprecedented pace and regulations struggling to keep up, this timely and comprehensive guide serves as an indispensable resource for practitioners.

Ben Steiner, Columbia University

Machine learning models are very complex in nature and their development is fraught with pitfalls. Mistakes in this field can cost manys reputation and millions or even billions of dollars. This book contains must-have knowledge for any machine learning practitioner who wants to design, develop, and deploy robust machine learning models that avoid failing like so many other ML endeavors over the past years.

Szilard Pafka, PhD, Chief Scientist, Epoch

Saying this book is timely is an understatement. People who do machine learning models need a text like this to help them consider all the possible biases and repercussions that arise from the models they create. The best part is that Patrick, James, and Parul do a wonderful job in making this book readable and digestible. This book is needed on any machine learning practitioners bookshelf.

Aric LaBarr, PhD, Associate Professor of Analytics

This is an extremely timely book. Practitioners of data science and AI need to seriously consider the real-world impact and consequences of models. The book motivates and helps them to do so. It not only provides solid technical information, but weaves a cohesive tapestry with legislation, security, governance, and ethical threads. Highly recommended as reference material.

Jorge Silva, PhD, Director of AI/Machine Learning Server, SAS

With the ever-growing applications of AI affecting every facet of our lives, it is important to ensure that AI applications, especially the ones that are safety critical, are developed responsibly. Patrick Hall and team have done a fantastic job in articulating the key aspects and issues in developing safety-critical applications in this book in a pragmatic way. I highly recommend this book, especially if you are involved in building AI applications that are high stakes, critical, and need to be developed and tested systematically and responsibly!

Sri Krishnamurthy, QuantUniversity

If youre looking for direction from a trusted advisor as you venture into the use of AI in your organization, this book is a great place to start. The authors write from a position of both knowledge and experience, providing just the right mix of baseline education in technology and common pitfalls, coverage of regulatory and societal issues, relevant and relatable case studies, and practical guidance throughout.

Brett Wujek, PhD, Head of AI Product Management, SAS