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Developing Trust in Artificial Intelligence and Machine Learning for High-Stakes Applications
This virtual talk is a featured presentation of the National Science Foundation Research Experience for Undergraduates (NSF REU) on Computational Methods for Understanding Music, Media, and Minds. The talk is free and open to all faculty, staff, students and community members.
Abstract: As machine learning models are increasingly supporting decision making in high-stakes applications such as healthcare, finance, education, and criminal justice, it is critical that we trust these models. In this talk, I will argue that in order to build that trust, we must be concerned with more than just accuracy. Safety and security (including fairness, robustness to adversarial attacks, and robustness to dataset shift), transparency (including explainability and factsheets), and a purpose that aligns with the values of society are all needed too.
Bio: 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 the 2013 Gerstner Award for Client Excellence for contributions to the WellPoint team and the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation, and Harvard Belfer Center Tech Spotlight runner-up for AI Fairness 360. 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. He is a senior member of the IEEE.