A Tutorial on Contemporary Machine Learning Risk Management


Artificial intelligence and machine learning (AI/ML) are important technologies with disruptive potential for industry, government, and the public. Yet, the current generation of AI/ML systems is often hindered by sociological bias and quality assurance issues. As headlines portray, you can succeed financially in AI/ML today without fairness and governance, but you might also get yourself sued, find yourself face-to-face with regulators, or harm many people. This talk will introduce recent AI incidents as the motivation for discussing governance in AI/ML. It will then address the fundamentals of model risk management and accountability structures for AI/ML systems, and also introduce novel approaches and technologies that can enhance AI/ML risk mitigation efforts. With government and public scrutiny on the rise, AI/ML's wild west days could end sooner rather than later. Join this talk to learn some basics about making a better future for these ever more present technologies.


Patrick Hall is principal scientist at BNH.AI, where he advises Fortune 500 clients on matters of AI risk and conducts research on AI risk management in support of NIST's efforts on trustworthy AI and technical AI standards. He also serves as visiting faculty in the Department of Decision Sciences at The George Washington School of Business, teaching classes on data ethics, machine learning, and the responsible use thereof.

Prior to co-founding BNH, Patrick led H2O.ai’s efforts in responsible AI, resulting in one of the world’s first commercial solutions for explainable and fair 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's technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch and others. An ardent writer himself, Patrick has contributed pieces to outlets like McKinsey.com, O'Reilly Ideas, Thompson-Reuters Regulatory Intelligence, and he is the lead author for the forthcoming book, Machine Learning for High Risk Applications.

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