A Carative Approach to AI Governance – Kush R. Varshney скачать в хорошем качестве

A Carative Approach to AI Governance – Kush R. Varshney

Institute for Assured Autonomy & Computer Science Seminar Series April 19, 2022 “A Carative Approach to AI Governance” Kush R. Varshney, IBM Research In recent times, we often hear a call for the governance of AI systems, but what does that really mean? In this talk, Kush R. Varshney will first adopt a control theory perspective to explain governance that determines the reference input via value alignment, data scientists acting as the controller to meet the values in a machine learning system, and facts captured in transparent documentation as the feedback signal. He will later go into further depth on value alignment via CP-nets and performance metric elicitation, as well as AI testing and transparency via factsheets. He will conclude by adopting a nursing theory perspective to explain how the control theory perspective lacks caring and the need for a carative approach that starts with the real-world problem as experienced by the most vulnerable people. Kush R. Varshney was born in Syracuse, New York in 1982. He received his BS (magna cum laude) in electrical and computer engineering with honors from Cornell University in 2004. He received his SM in 2006 and PhD in 2010, both in electrical engineering and computer science, from the Massachusetts Institute of Technology. While at MIT, he was an NSF Graduate Research Fellow. Varshney is a distinguished research staff member and manager with IBM Research at the Thomas J. Watson Research Center in Yorktown Heights, New York, where he leads the machine learning group in the Foundations of Trustworthy AI Department. Varshney was a visiting scientist at IBM Research - Africa in Nairobi, Kenya in 2019. He is the founding co-director of the IBM Science for Social Good initiative. Varshney applies data science and predictive analytics to human capital management, health care, 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, the Extraordinary IBM Research Technical Accomplishment for contributions to workforce innovation and enterprise transformation, and the Harvard Belfer Center Tech Spotlight runner-up for AI Fairness 360. Varshney conducts academic research on the theory and methods of trustworthy machine learning. His work has been recognized through Best Paper Awards at Fusion 2009, the 2013 Institute of Electrical and Electronics Engineers (IEEE) International Conference on Service Operations and Logistics, and Informatics 2013, the 2014 ACM Special Interest Group on Knowledge Discovery and Data Mining conference, the 2015 Society for Industrial and Applied Mathematics International Conference on Data Mining, and the 2019 Computing Community Consortium/Schmidt Futures Computer Science for Social Good White Paper Competition. He self-published a book entitled Trustworthy Machine Learning in 2021 and is a senior member of the IEEE.