Krishnaram Kenthapadi

Chief Scientist, Clinical AI, Oracle Health

Krishnaram Kenthapadi is the Chief Scientist, Clinical AI at Oracle Health, where he leads the AI initiatives for Clinical Digital Assistant and other Oracle Health products. Previously, as the Chief AI Officer & Chief Scientist of Fiddler AI, he led initiatives on generative AI (e.g., Fiddler Auditor, an open-source library for evaluating & red-teaming LLMs before deployment; AI safety, observability & feedback mechanisms for LLMs in production), and on AI safety, alignment, observability, and trustworthiness, as well as the technical strategy, innovation, and thought leadership for Fiddler. Prior to that, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform, and shaped new initiatives such as Amazon SageMaker Clarify from inception to launch. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 60+ papers, with 7000+ citations and filed 150+ patents (72 granted). He has presented tutorials on trustworthy generative AI, privacy, fairness, explainable AI, model monitoring, and responsible AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.

Krishnaram Kenthapadi

Sessions / 2025 / 1 talk

  • Join us for a keynote panel that moves beyond theoretical discussions of AI ethics to explore the practical realities of implementing responsible AI safeguards. This conversation will unpack the complex trade-offs and technical challenges faced when deploying AI systems at scale. Panelists will share insights from building fairness and privacy protections into major platforms while maintaining innovation, and discuss how responsible AI has evolved into a business imperative. Topics include creating effective trust and safety protocols for generative AI, developing robust safeguards for user-generated content, implementing fairness frameworks across diverse products, and managing the tension between rapid deployment and thorough safety testing. Expect candid discussion about governance structures that work, persistent technical hurdles, and lessons learned from high-stakes incidents that shaped today's AI safeguards.

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