Patrick Hall is the principal scientist at HallResearch.ai. He is also an assistant professor of decision sciences at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. Patrick conducts research in support of NIST's AI Risk Management Framework, works with leading fair lending and AI risk management advisory firms, and serves on the board of directors for the AI Incident Database. Prior to 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; he also led H2O.ai's efforts in the development of responsible AI, resulting in one of the world's first commercial applications for explainability and bias mitigation in machine learning. Patrick started his career in global customer-facing roles and R&D 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. He 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”), and the American Statistical Association Joint Statistical Meetings. He has been published in outlets like Information, Frontiers in AI, McKinsey.com, O'Reilly Media, and Thomson Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, WIRED, InfoWorld, TechCrunch, and others. Patrick is the lead author of the book Machine Learning for High-Risk Applications.

With affiliations across private industry, civil society, academia, and government, Patrick brings one of the widest possible perspectives to AI and matters of risk. He has built machine learning software solutions and advised on AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and US and foreign government agencies.

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