Krishnaram Kenthapadi

Krishnaram Kenthapadi

Chief AI Officer & Chief Scientist at Fiddler AI

Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, 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. 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 50+ papers, with 7000+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative 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.

All Sessions by Krishnaram Kenthapadi

Tutorial: Deploying Trustworthy Generative AI

Generative AI | All Levels

Generative AI models and applications are being rapidly deployed across several industries, but there are several ethical and social considerations that need to be addressed. These concerns include lack of interpretability, bias and discrimination, privacy, lack of model robustness, fake and misleading content, copyright implications, plagiarism, and environmental impact associated with training and inference of generative AI models. In this talk, we first motivate the need for adopting responsible AI principles when developing and deploying large language models (LLMs) and other generative AI models, and provide a roadmap for thinking about responsible AI for generative AI in practice. Focusing on real-world LLM use cases (e.g. evaluating LLMs for robustness, security, etc. using, we present practical solution approaches / guidelines for applying responsible AI techniques effectively and discuss lessons learned from deploying responsible AI approaches for generative AI applications in practice. By providing real-world generative AI use cases, lessons learned, and best practices, this talk will enable researchers & practitioners to build more reliable and trustworthy generative AI applications. Please take a look at our recent ICML/KDD/FAccT tutorial ( for an expanded version of this talk.

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