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Beyond Accuracy : What does it take to trust AI decisions

Now that AI is being used in high risk applications with serious consequences, it is important that it be worthy of society’s trust. In this presentation, we will sketch out what it means for an AI system to be trustworthy, including more well-known characteristics like fairness, explainability, and robustness, but also emerging characteristics such as causality, transparency, and uncertainty quantification. You will learn how to work towards these goals throughout the machine learning lifecycle.

Kush R. Varshney was born in Syracuse, NY in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, NY, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge. While at MIT, he was a National Science Foundation Graduate Research Fellow.
Dr. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center, Yorktown Heights, NY, where he leads the machine learning group in the Foundations of Trustworthy AI department. He was a visiting scientist at IBM Research – Africa, Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. He applies data science and predictive analytics to human capital management, healthcare, olfaction, computational creativity, public affairs, international development, and algorithmic fairness, which has led to recognitions such as IBM Corporate Technical Awards for AI-Powered Employee Journey and for Trustworthy AI. He conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through best paper awards at the Fusion 2009, SOLI 2013, KDD 2014, and SDM 2015 conferences and the 2019 Computing Community Consortium / Schmidt Futures Computer Science for Social Good White Paper Competition. He is currently writing a book entitled ‘Trust in Machine Learning’ with Manning Publications.