Social Choice for Fair Recommendations

Recommender systems influence nearly every aspect of our digital lives—but what does it mean for those systems to be fair? Robin Burke joins Data Skeptic to discuss the history of recommender systems, the limitations of optimizing purely for accuracy, and how ideas from social choice theory can help balance the needs of users, creators, and society. The conversation explores the future of recommendation algorithms and why fairness is a far more complex challenge than it first appears.

Guest

Robin Burke: Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore 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. Dr Burke obtained his PhD in Computer Science from Northwestern University in 1993, an MPhil in Computer Science from Yale University in 1990 and a BS in Computer Science from Harvey Mudd College in 1986. In addition to DePaul University, he has held positions at the University of Chicago, the University of California – Irvine, California State University – Fullerton and University College Dublin. He was co-director of DePaul’s Center for Web Intelligence, and a member of DePaul’s interdisciplinary Data Mining and Predictive Analytics Center and also of Studio χ, DePaul’s digital humanities center. Professor Burke is the author of more than 70 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.