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User, Agent, Subject, Spy: Information Systems for Human Flourishing - Michael Ekstrand

March 5 @ 10:30 am - 11:30 am MST

Michael Ekstrand
Assistant Professor, Computer Science
Boise State University

Thursday, March 5th, at 10:30 AM in CCP Room 259

Abstract: Every day, information access systems mediate our experience of the world beyond our immediate senses. Google and Bing help us find what we seek, Amazon and Netflix recommend things for us to buy and watch, Apple News gives us the day’s events, and BuzzFeed guides us to related articles. These systems deliver immense value, but also have a profound influence on how we experience information and the resources and perspectives we see. There are significant challenges, however, in measuring this influence and characterizing the benefits and harms these systems deliver to the various people they affect.

Through a combination of system-building, experimentation, and data analysis, we are working to ensure information systems are responsive to the needs and well-being of the people they affect. I will report on several projects on understanding users’ information needs in educational contexts, quantifying systematic biases in evaluating the ability of recommender systems to provide users with useful recommendations, and describing the interaction of collaborative filters with potentially discriminatory biases in production and consumption data.

Biography: Dr. Michael Ekstrand is an assistant professor in the Department of Computer Science at Boise State University, where he co-directs the People and Information Research Team (PIReT). Grounded in human-computer interaction with heavy doses of software engineering, machine learning, and information retrieval, his research agenda is to make sure that information access systems — particularly recommender systems — are good for all the people they affect. He does this through a combination of data analysis, simulation, and system-building. 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.

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 Fairness track at TREC, and more broadly as one of the FAT* Network co-chairs.