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Title: Lessons from Bottom of the Pyramid Innovation for AI for Social Good
Abstract: The AI for Social Good movement is nearing a full decade, but most projects in the space have had little meaningful and sustained impact beyond the purpose of demonstration. One of the main reasons for this is that, like the populations they serve, social change organizations tend to be stretched thin in terms of money, time, and investment in capacity building. Specifically, they have limited capacity to take custom solutions created for them by volunteer data scientists and deploy/maintain them in their operations due to skill and resource gaps. As an alternative, we conjecture that highly usable and reusable software toolkits delivered as a service through cloud-based platforms containing fair and robust models relevant for the missions of several social change organizations can overcome the operationalization gap for the 'bottom of the pyramid' of organizations. In this talk, we will describe the conjecture as well as the experiments we have conducted so far to validate it.
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 'Trustworthy Machine Learning' with Manning Publications. He is a senior member of the IEEE and a member of the Partnership on AI's Safety-Critical AI expert group.