Dr. Michael Ekstrand 

Speech Title: Searching for Fairness: Grounding and Measuring Fairness and Social Impacts in Information Access
 
Abstract

Information access systems, such as search engines, recommender systems, and conversational agents, are used daily by billions of Internet users and have a profound impact on users’ information experiences, access to knowledge, and understanding of the world and people around them. These systems differ in crucial ways from the kinds of systems most frequently studied in the algorithmic fairness literature, requiring new techniques to properly understand and measure their social impacts. In this talk, I will discuss what makes these systems different and interesting; ground the quest for fairness and mitigating social harms in relevant ethical and legal considerations; and describe key considerations in measuring and mitigating harms that also apply beyond information access to a wider range of computing systems.

Biography

Michael Ekstrand is an assistant professor of information science at Drexel University, where he leads the Impact, Novation, Effectiveness, and Responsibility of Technology for Information Access Lab (INERTIAL). His research blends human-computer interaction, information retrieval, machine learning, and statistics to try to make information access systems, such as recommender systems and search engines, good for everyone they affect. In 2018, he received the NSF CAREER award to study how recommender systems respond to biases in input data and experimental protocols and predict their future response under various technical and sociological conditions, and is co-PI on the NSF-funded POPROX project to develop shared infrastructure for user-facing recommender systems research.

Previously he was faculty at Boise State University, where he co-led the People and Information Research Team, and earned his Ph.D. in 2014 from the University of Minnesota. He leads the LensKit open-source software project for enabling high-velocity reproducible research in recommender systems and co-created the Recommender Systems specialization on Coursera with Joseph A. Konstan from the University of Minnesota. He has worked to develop and support communities studying fairness and accountability, both within information access through the FATREC and FACTS-IR workshops and the Fair Ranking track at TREC, and more broadly through the ACM FAccT community in various roles.