Next QConSF Conference: Applied AI for Developers QCon.ai April 2019

Speaker: Krishnaram Kenthapadi

Tech Lead Fairness, Transparency, Explainability & Privacy Efforts @LinkedIn

Krishnaram Kenthapadi is part of the AI team at LinkedIn, where he leads the fairness, transparency, explainability, and privacy modeling efforts across different LinkedIn applications. He also serves as LinkedIn's representative in Microsoft's AI and Ethics in Engineering and Research (AETHER) Committee. 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. He 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. He received Microsoft's AI/ML conference (MLADS) distinguished contribution award, CIKM best case studies paper award, SODA best student paper award, and WWW best paper award nomination. He has published 35+ papers, with 2500+ citations and filed 130+ patents. He has taught a tutorial on privacy-preserving data mining at KDD 2018, instructed a course on artificial intelligence at Stanford, and given several talks on his research work.

Find Krishnaram Kenthapadi at

LinkedIn

Talk : Fairness, Transparency, and Privacy in AI @LinkedIn

Track : Applied AI & Machine Learning
Location : Ballroom BC
Duration : 2:55pm - 3:45pm

How do we protect privacy of users in large-scale systems? How do we ensure fairness and transparency when developing machine learned models? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical and legal challenges encountered by researchers and practitioners alike. In this talk, we will first present an overview of privacy breaches as well as algorithmic bias / discrimination issues observed in the Internet industry over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving privacy and fairness in data-driven systems. We will motivate the need for adopting a "privacy and fairness by design" approach when developing data-driven AI/ML models and systems for different consumer and enterprise applications. We will also focus on the application of privacy-preserving data mining and fairness-aware machine learning techniques in practice, by presenting case studies spanning different LinkedIn applications, and conclude with the key takeaways and open challenges.

Other talks from track Applied AI & Machine Learning

Jupyter Notebooks: Interactive Visualization Approaches
Senior Researcher in the Quantitative Financial Research Group @Bloomberg
Chakri Cherukuri
Nearline Recommendations for Active Communities @LinkedIn
Senior Manager & Heading AI for Growth and Communication Relevance @LinkedIn
Hema Raghavan
Deep Representation: Building a Semantic Image Search Engine
Head of AI @InsightDataSci
Emmanuel Ameisen
Human-Centric Machine Learning Infrastructure @Netflix
Machine Learning Infrastructure Engineer @Netflix
Ville Tuulos

Tracks

Monday, 5 November

Tuesday, 6 November

Wednesday, 7 November

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