Abstract: Trustworthy AI includes many different concepts, including predictive performance, fairness, robustness, explainability, uncertainty quantification, and transparency. In this talk, I will explain a common framework of trustworthiness out of which these concepts fall out and highlight how to approach them using AI Fairness 360, Adversarial Robustness 360, AI Explainability 360, Uncertainty Quantification 360, and AI FactSheets 360.
Bio: 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. 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.