Fairness, Explainability, and Privacy in AI/ML SystemsExport this event to calendar

Monday, April 12, 2021 — 4:00 PM to 5:00 PM EDT

Stanley Bak
Krishnaram Kenthapadi
Amazon AWS AI

Date: Monday, April 12th, 2021 @ 4PM EST.
Talk Title: Fairness, Explainability, and Privacy in AI/ML Systems
Abstract: How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? How do we protect the privacy of users when building large-scale AI based systems? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains such as lending and healthcare requiring reliability, safety, and fairness.
We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of fairness-aware ML, explainable AI, and privacy-preserving AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Bio: Krishnaram Kenthapadi is a Principal Scientist at Amazon AWS AI, where he leads the fairness, explainability, and privacy initiatives in Amazon AI platform. Until recently, he led similar efforts at LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research Advisory Board. Previously, he was a Researcher at Microsoft Research, where his work resulted in product impact (and Gold Star / Technology Transfer awards), and several publications/patents. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the program committees of 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. He has published 40+ papers, with 2500+ citations and filed 140+ patents (30+ granted). He has presented lectures/tutorials on privacyfairness, and explainable AI at KDD ’18 ’19, WSDM ’19, WWW ’19 ’20, FAccT ’20, and AAAI ’20.
Meeting Link: WebEx

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