This page lists my current research seminar and other standing talks. I can give any of these lectures with relatively short notice.
This is my normal research seminar, primarily targeted for researchers and advanced undergraduate students in computer science.
Title: Recommending for People
Recommender systems help people find movies to watch, introduce new friends on social networks, increase sales for online retailers by connecting their customers with personally-relevant products, and direct readers to additional articles on news publishers’ partner sites. Users interact with recommenders almost everywhere they turn on the modern Internet. However, there is a great deal we still do not yet know about how to best design these systems to support their users’ needs and decision-making processes, and how the recommender and its sociotechnical context support and affect each other.
In this talk, I will present work on understanding the ways in which different recommender algorithms impact and respond to their users. This research applies several methodologies, including analysis of recommender algorithms on public data sets and studies of both the stated preferences and observable behaviors of the users of a recommender system. Our findings provide evidence, consistent across different experimental settings, that different users are more satisfied by different recommendation algorithms even within the single domain of movie recommendation. I will also discuss our ongoing work on how recommender algorithms interact with biases in their underlying input data and on deep challenges in evaluating recommender effectiveness with respect to actual user needs.
These projects, along with several others, are a part of our broad vision for designing recommenders and other algorithmic information systems that are responsive to the needs, desires, and well-being of the people they will affect.
This talk presents a higher-level view of my research work, targeted at undergraduate students who are not necessarily in computer science.
Title: Making Information Systems Good for People
Every day, we interact with countless information systems enhanced by artificial intelligence: Google helps us find what we seek, Amazon and Netflix recommend things for us to buy and watch, Apple News gives us the day's events, BuzzFeed guides us to related articles, and VISA assesses whether our morning coffee is a fraudulent purchase. These systems deliver immense value, but also have profound influence over how we experience information and the resources and perspectives we see.
In this talk, I will explain the basic principles of modern data-driven artificial intelligence systems, and present some of the work that I and others are doing to measure their human impact and ensure that they are good for the people they affect: that they provide value, minimize harm, and produce outcomes that treat people fairly. I will pay particular attention to recommender systems, the algorithms that suggest products and articles, and how they do or do not meet their users' needs.
Dr. Michael Ekstrand is an assistant professor in the Department of Computer Science at Boise State University, where he co-leads the People and Information Research Team (PIReT). He studies human-computer interaction and artificial intelligence, working to ensure intelligent information systems — particularly recommender systems — are good for the people they affect. He received his Ph.D in 2014 from the University of Minnesota, supporting reproducible research and examining user-relevant differences in recommender algorithms with the GroupLens research group; is the founder and lead developer of LensKit, an open-source software project aimed at supporting reproducible research and education in recommender systems; and co-created the Recommender Systems specialization on Coursera.