Current Talks

This page lists my current research seminar and other standing talks. I can give any of these lectures with relatively short notice. I list them below with approximate time slot lengths, including questions; I can adjust to fit your schedule.

Research Seminar (1hr)
Undergraduate Seminar (1hr)
Responsible Recommendation (20min)

I will also consider invitations to give talks on the social impact of recommender systems, particularly with regards to bias and discrimination; principles of recommender systems; and evaluating recommender systems.

Research Seminar

This is my normal research seminar, primarily targeted for researchers and advanced undergraduate students in computer science, information science, and related disciplines. I am continuously updating this talk; its last major rewrite was Fall 2018.

Title: User, Agent, Subject, Spy: Information Systems for Human Flourishing
Length: 50 minutes (plus questions)

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 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.

Previous Installments

I have given this talk several times; video is available for some versions:

Undergraduate Seminar

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
Length: 50 minutes (plus questions)

Abstract

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.

Times Given

Whitman College

Responsible Recommendation

This is a shorter, higher-level talk on ethics and user engagement in designing and evaluating recommender systems.

Title: Responsible Recommendation
Length: 20 minutes

I gave this talk at the Brussels Data Ethics meeting.

Bio

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