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Presentation: Putting Fairness Principles Into Practice

Track: Deep Learning in Practice

Location: Cyril Magnin II

Duration: 2:20pm - 3:00pm

Day of week: Tuesday

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Abstract

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road. In this talk I present a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.

Speaker: Alex Beutel

Senior Research Scientist @Google

Alex Beutel is a Senior Research Scientist in the Google Brain SIR team working on neural recommendation, fairness in machine learning, and ML for Systems. He received his Ph.D. in 2016 from Carnegie Mellon University’s Computer Science Department, and previously received his B.S. from Duke University in computer science and physics. His Ph.D. thesis on large-scale user behavior modeling, covering recommender systems, fraud detection, and scalable machine learning, was given the SIGKDD 2017 Doctoral Dissertation Award Runner-Up. He received the Best Paper Award at KDD 2016 and ACM GIS 2010, was a finalist for best paper in KDD 2014 and ASONAM 2012, and was awarded the Facebook Fellowship in 2013 and the NSF Graduate Research Fellowship in 2011. More details can be found at alexbeutel.com.

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