Every day, people use recommender systems, search engines, generative AI tools, and other information access systems to locate, collate, and understand information. These tools in turn shape their users’ understanding of the information space, and through it the world and society they inhabit. Information access has profound possibilities for connecting people with information, products, culture, and people they wouldn’t encounter in other ways, but can also reproduce a range of harms including discrimination, negative or unnecessary stereotypes, information silos, and more. I am concerned with ensuring these systems and their effects are beneficial: that their benefits are available to everyone, they are built on and with principles upholding human rights and dignity, and they are accountable to the people they will impact.
In this talk, I will discuss efforts to map the landscape of social impacts and harms and to achieve the positive promise of effective and equitable information access, with a particular emphasis on developing constructs and measurements to assess the extent to which information access systems advance or impede efforts to build inclusive, equitable, and democratic societies.
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 he 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. He is also currently on the Executive Committee for the ACM RecSys.