Dr. Michael Ekstrand‘s talk
Date and Time: Saturday Nov 19, 2022 16:00-17:00
Equity and Discrimination in Information Access
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, discussing both the state of the art in measuring relatively well-understood harms and new directions and open problems in defining and measuring fairness problems.
Michael Ekstrand is an associate professor of computer science at Boise State University, where he co-directs the People and Information Research Team. His research blends information retrieval, human-computer interaction, machine learning, and algorithmic fairness to try to make information access systems, such as recommender systems and search engines, good for everyone they affect. In 2018, he receieved 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.
He received his Ph.D in 2014 from the University of Minnesota, building tools to support reproducible research and examining user-relevant differences in recommender algorithms with the GroupLens research group. 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 is currently working 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.
His monograph Fairness in Information Access Systems is available for free (with registration) during Nov 18-21!
TREC Fair Ranking Track run by Michael and friends
NTCIR Fair Web Task run by the Sakai Lab and friends