Jamie Morgenstern

Jamie Morgenstern is a computer scientist who studies the social impact of machine learning, including predictive inequity, in which systems perform unevenly for different demographic groups. After a doctorate under Avrim Blum at Carnegie Mellon in 2015, she was a Warren Center fellow at the University of Pennsylvania and coauthored Fairness in Learning: Classic and Contextual Bandits in 2016. At Georgia Tech, where she became an assistant professor in 2018, she served as a program chair of the Fairness, Accountability, and Transparency conference in 2019. Her coauthored papers include Datasheets for Datasets, which proposes documentation for every dataset, and Predictive Inequity in Object Detection, which found uneven detection performance for pedestrians with different skin types. She moved to the University of Washington in 2019, received an NSF CAREER Award in 2021, and is an associate professor in the Allen School of Computer Science & Engineering there.

Now at: University of Washington

Timeline

2010–2015

A Ph.D. at Carnegie Mellon

She earns a double bachelor's degree in computer science and mathematics at the University of Chicago in 2010. At Carnegie Mellon University, she studies under Avrim Blum, receives a master's degree in 2012, and completes her Ph.D. in 2015 with the dissertation Market Algorithms: Incentives, Learning, and Privacy.
By 2018

Warren Fellow at Penn

As a Warren Center fellow at the University of Pennsylvania, she is hosted by Michael Kearns, Aaron Roth and Rakesh Vohra. In 2016, with Matthew Joseph, Michael Kearns and Aaron Roth, she coauthors Fairness in Learning: Classic and Contextual Bandits, which introduces the study of fairness in multi-armed bandit problems.
2018–2019

Assistant professor at Georgia Tech

She joins the faculty of Georgia Tech's School of Computer Science as an assistant professor in Spring 2018, choosing the school for its reputation as a theoretical computer science hub. In January 2019, she serves with danah boyd as a program chair of the Fairness, Accountability, and Transparency conference in Atlanta.
2018–2021

Datasheets for Datasets

Datasheets for Datasets, posted in 2018, lists her as a coauthor with Timnit Gebru, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III and Kate Crawford. The paper proposes that every dataset come with a datasheet describing its motivation, composition, collection process and recommended uses, and it appears in CACM in December 2021.
2019

Joining the Allen School

In 2019, she moves from Georgia Tech to the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Her research there explores the social impact of machine learning, including predictive inequity, where systems perform unevenly for different demographic groups.
2019

Predictive inequity in object detection

With Benjamin Wilson and Judy Hoffman, she studies whether object detection systems perform equally well for pedestrians with different skin tones, annotating the BDD100K dataset with Fitzpatrick skin tones. Their 2019 paper finds that detection performance varies for pedestrians with different skin types, independent of time of day or several other potentially mitigating factors.
2021

An NSF CAREER Award

She wins a National Science Foundation CAREER Award in 2021 for Strategic and Equity Considerations in Machine Learning. The project examines machine learning systems trained and tested on heterogeneous and strategically generated data that make both predictions and decisions.
As of October 2026

Associate professor at the Allen School

She is an associate professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, with expertise in ethics and fairness, human-centered AI, and machine learning. Her research asks how machine learning can be made robust to the behavior of the people who generate its training or test data, and how models can avoid worsening inequalities already present in society.