Title: User Aspects of Fairness-Aware Recommendation

Presenter: Department Chair, Information Science, Univeristy of Colorado Boulder

Abstract: Recommender systems are machine learning systems that provide personalized results to users across a wide array of applications from social media to e-commerce to news to online dating. As fairness in machine learning has become a major sub-field of research in the past five years, recommender systems have also benefited from this emphasis. However, as these fairness-aware systems begin to be deployed, it becomes quite clear that we know very little about how users think about and interact with systems that take ethical stances, stances which might put them at odds with user interests and goals. This talk will discuss some recent research into these issues, highlight some of open questions and consider potential research that might address them.

Bio: Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. Among other topics, his research group, That Recommender Systems Lab, explores fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. He joined the Department of Information Science in 2019 from the School of Computing at DePaul University. Professor Burke is the author of more than 150 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.
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