In this episode of ACM ByteCast, Bruke Kifle hosts Kush Varshney, a distinguished research scientist and manager at IBM Research in New York. He leads the machine learning group in the Foundations of Trustworthy AI Department, where he applies data science and predictive analytics to the fields of healthcare, public affairs, algorithmic fairness, and international development. He is also the founding co-director of the IBM Science for Social Good initiative. He has contributed to the development of several open-source toolkits such as AI Fairness 360 and AI Explainability 360. In 2022, he independently published the book Trustworthy Machine Learning. Kush has been recognized with the Extraordinary IBM Research Technical Accomplishment Award for contributions to workforce innovation and enterprise transformation, and IBM Corporate Technical Awards for Trustworthy AI and for AI-Powered Employee Journey.

Kush shares a few key moments which have helped to shape the course of his career thus far, including his graduate days at MIT and joining IBM Research. He defines responsible AI and talks about operationalizing RAI principles, as well as the importance of finding a balance between the technical and social aspects of AI. He also discusses some of the risks—both short- and long-term—inherent in emerging technologies such as generative AI, and how various stakeholders can play a role in coordinating AI safety. Kush also mentions his book, his work with IBM’s Science for Social Good, and some of the things that excite him about the future of AI.