Adopting Language Models Requires Risk Management — This is How

Abstract: 

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Bio: 

Patrick Hall is an assistant professor of decision sciences at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. He also conducts research in support of NIST's AI risk management framework and is affiliated with leading fair lending and AI risk management advisory firms.

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 Science, Engineering, and Medicine, ACM SIG-KDD, and the Joint Statistical Meetings. He has been published in outlets like Information, Frontiers in AI, McKinsey.com, O'Reilly Ideas, and Thompson-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.

Prior to joining the GW School of Business, Patrick co-founded BNH.AI, a boutique law firm focused on AI governance and risk management. He 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. Patrick also held global customer-facing roles and R&D roles at SAS Institute. Patrick has built machine learning software solutions and advised on matters of AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and U.S. and foreign government agencies.

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