Patrick Hall

Patrick Hall is the principal scientist at HallResearch.ai, conducting research in support of NIST’s AI Risk Management Framework, working with leading fair lending and AI risk advisory firms, and serving on the board of directors for the AI Incident Database. He is also teaching faculty in the Department of Decision Sciences at the George Washington University School of Business, lecturing on AI ethics, business analytics, and machine learning.

Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. Since then, he has built machine learning software solutions and advised on AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and government agencies. He started his career in global customer-facing and R&D roles at SAS Institute. Patrick then led H2O.ai’s efforts in developing responsible AI, resulting in one of the world’s first commercial applications for explainability and bias mitigation in machine learning. Before co-founding HallResearch.ai, Patrick was a partner at BNH.AI, where he pioneered the emergent discipline of auditing and red-teaming generative AI systems.

With affiliations across private industry, civil society, academia, and government, Patrick brings one of the broadest possible perspectives to AI and matters of risk. Patrick has been invited to speak on AI and machine learning topics at the National Academies of Sciences, Engineering, and Medicine, the Association for Computing Machinery SIG-KDD (“KDD”) conference, and the American Statistical Association Joint Statistical Meetings. Patrick is the lead author of the book Machine Learning for High-Risk Applications. He has also been published in outlets like NIST, Information, Frontiers in AI, McKinsey.com, and Thomson Reuters Regulatory Intelligence, and his expertise has been featured in the New York Times, Fortune, WIRED, InfoWorld, TechCrunch, and the American Banker AI 100 list.