The Academic Fringe Festival - Responsible AI in Industry: Practical Challenges and Lessons Learned
Responsible Use of Data seminar series
26 juli 2021 17:00 | Zet in mijn agenda
by Krishnaram Kenthapadi | Amazon AWS AI
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 hiring, lending, and healthcare. 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 responsible 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.
Speaker Biography
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 the 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 50+ papers, with 4500+ citations and filed 145+ patents (65 granted). He has presented tutorials on privacy, fairness, explainable AI, and responsible AI at forums such as KDD ’18 ’19, WSDM ’19, WWW ’19 ’20 '21, FAccT ’20 '21, and AAAI ’20 '21.
More information
In this second edition on the topic of "Responsible Use of Data", we take a multi-disciplinary view and explore further lessons learned from success stories and examples in which the irresponsible use of data can create and foster inequality and inequity, perpetuate bias and prejudice, or produce unlawful or unethical outcomes. Our aim is to discuss and draw certain guidelines to make the use of data a responsible practice.
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