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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 cofounding 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. He has contributed written pieces to outlets like McKinsey.com, O'Reilly Radar, and Thompson Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch, and others.