The AI Ethics & Fairness track covers various topics related to the ethical implications of the work we do as data professionals (understanding bias, privacy, data security, etc.). Some companies are just getting started with data, some have a more mature data team and process, and some simply have too much data and too small of a team to manage it!
On top of it all, many are unaware, or perhaps unconcerned about the ethical implications of their work. Assembling folks who are not afraid to openly discuss current challenges in the adoption of cutting-edge AI technologies is the best way to further the development of mature data organizations across industries. This track aims to share and spread that knowledge.
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 across different LinkedIn applications as part of the LinkedIn AI team, and served as LinkedIn's representative in Microsoft's AI and Ethics in Engineering and Research (AETHER) Advisory Board.
He shaped the technical roadmap and led the privacy/modeling efforts for LinkedIn Salary product, and prior to that, served as the relevance lead for the LinkedIn Careers and Talent Solutions Relevance team, which powers search/recommendation products at the intersection of members, recruiters, and career opportunities. Previously, he was a Researcher at Microsoft Research Silicon Valley, 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, and his Bachelors in Computer Science from IIT Madras.
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. He received Microsoft's AI/ML conference (MLADS) distinguished contribution award, NAACL best thematic paper award, CIKM best case studies paper award, SODA best student paper award, and WWW best paper award nomination. He has published 40+ papers, with 2500+ citations and filed 140+ patents (30+ granted). He has presented lectures/tutorials on privacy (https://sites.google.com/view/privacy-tutorial
), fairness (https://sites.google.com/view/fairness-tutorial
), and explainable AI (https://sites.google.com/view/explainable-ai-tutorial
) in industry at forums such as KDD '18 '19, WSDM '19, WWW '19, FAccT '20, and AAAI'20 , and instructed a course on AI at Stanford.