Trustworthy Machine Learning and Artificial Intelligence

In this episode of the IEEE Signal Processing Society podcast, Dr. Lav Varshney, Associate Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign interviews Dr. Kush Varshney, an IBM Fellow and globally recognized expert in trustworthy machine learning. Their conversation explores the multifaceted landscape of trustworthy AI.

Kush Varshney

Kush R. Varshney is an IBM Fellow at IBM Research and a leading authority on trustworthy AI. His work focuses on making AI systems not only accurate but also fair, robust, explainable, transparent, inclusive, and beneficial. He is the author of a book entitled “Trustworthy Machine Learning” and creator of widely used toolkits like AI Fairness 360 and AI Explainability 360.

In this episode, Dr. Varshney outlines the core principles of trustworthy AI and distinguishes it from related concepts such as AI ethics, AI safety, and responsible AI. He shares how signal processing techniques—like Boolean compressed sensing and continued fraction representations, and short-time Fourier transforms—inform his approach. The conversation covers the societal impact of AI, the shift toward generative and agentic models, the importance of governance and policy, and new research directions aimed at building more empowering and accountable AI systems.