Speaker: Michael Ekstrand, Drexel University (will be in person for this talk)
Talk Title: Search, Recommendation, and Sea Monsters: Mapping the Landscape of Fairness and Social Impact in Information Access
Date: Friday, May 9, 2025 - 1:30 - 2:30 PM EDT (North American Eastern Daylight Saving Time)
Abstract: Ensuring that information access systems are “fair”, or that their benefits are equitably experienced by everyone they affect, is a complex, multi-faceted problem. Significant progress has been made in recent years on identifying and measuring important forms of unfair recommendation and retrieval, but there are still many ways that information systems can replicate, exacerbate, or mitigate potentially discriminatory harms that need careful study. These harms can affect different stakeholders — such as the producers and consumers of information, among others — in many different ways, including denying them access to the system's benefits, misrepresenting them, or reinforcing unhelpful stereotypes.
In this talk, I will provide an overview of the landscape of fairness and anti-discrimination in information access systems and their underlying theories, discussing both the state of the art in measuring relatively well-understood harms and new directions and open problems in defining and measuring fairness and related social impact goals. I will also discuss the broader landscape of harms, impacts, and intervention strategies, and the need to think expansively and creatively about how to understand information access impacts and the possibilities for intervening to improve those impacts.
Bio: 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.
Zoom Access: Zoom Link and reach out to Hamed Zamani or Dan Parker for the passcode.
