Machine Learning for High-Risk Applications

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The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks.

This book describes approaches to responsible AI-a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

• Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security
• Learn how to create a successful and impactful AI risk management practice
• Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework
• Engage with interactive resources on GitHub and Colab

About the authors

James Curtis

James is a quantitative researcher at Solea Energy, where he is focused on using statistical forecasting to further the decarbonization of the US power grid. He was previously a consultant for financial services organizations, insurers, regulators, and health care providers, helping them build more equitable machine learning models. James holds an M.S. in Applied Mathematics from the Colorado School of Mines.

Patrick Hall

Patrick Hall is principal scientist at BNH.AI, where he advises Fortune 500 companies and cutting-edge startups on AI risk and conducts research in support of NIST's AI risk management framework. He also serves as visiting faculty in the Department of Decision Sciences at The George Washington School of Business, teaching data ethics, business analytics, and machine learning classes.

Before co-founding BNH, Patrick led H2O.ai's efforts in responsible AI, resulting in one of the world's first commercial applications for explainability and bias mitigation in machine learning. He also held global customer-facing roles and R&D research roles at SAS Institute. Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.

Patrick has been invited to speak on topics relating to explainable AI at the National Academies of Science, Engineering, and Medicine, ACM SIG-KDD, and the Joint Statistical Meetings. An ardent writer, Patrick has contributed pieces to outlets like McKinsey.com, O'Reilly Ideas, Thompson-Reuters Regulatory Intelligence, and he is an author of the book Machine Learning for High-risk Applications. His technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch, and others.