Current Talks

Michael (full resolution)

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.

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 reguarly update it with new results and perspectives, with a total refresh from time to time, so it’s never the same twice.

Title: Search, Recommendaion, and Sea Monsters Length: 50 minutes (plus questions)


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 and their underlying theories, 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.

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)


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

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 first gave this talk at the Brussels Data Ethics meeting.


Michael Ekstrand is an assistant professor of information science at Drexel University. 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 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.

Previously he was faculty at Boise State University, where he co-led the People and Information Reseach 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 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.