Kathleen (Katie) Creel
Assistant Professor
Research interests
- Algorithmic fairness, transparency, and explanation
- Monoculture and homogeneity due to use of AI
- Philosophy of science and science of science
Education
- PhD in History and Philosophy of Science, University of Pittsburgh
- MA in Philosophy, Simon Fraser University — Canada
- BA in Computer Science and Philosophy, Williams College
Biography
Kathleen Creel is an assistant professor at Northeastern University, holding joint appointments in the College of Social Sciences and Humanities and Khoury College of Computer Sciences, based in Boston.
Her research explores the moral, political, and epistemic implications of machine learning as it is used in automated decision-making and in science. Before joining Northeastern, Creel was the Embedded Ethics fellow in the Center for Ethics in Society and the Institute for Human-Centered Artificial Intelligence at Stanford University. In this role, she worked with the Stanford Computer Science department to embed ethics in the core curriculum. Creel worked as a software engineer at MIT Lincoln Laboratory and subsequently pursued her doctorate in history and philosophy of science from the University of Pittsburgh.
Creel’s research has been recognized with FAccT’s Best Paper Award, IACAP’s Herbert A. Simon Award for Outstanding Research in Computing and Philosophy, and the Philosophy of Science Association’s Ernst Nagel Early Career Scholar Essay Award.
Recent publications
-
Allocation Multiplicity: Evaluating the Promises of the Rashomon Set
Citation: Shomik Jain, Margaret Wang, Kathleen Creel, Ashia Wilson. (2025). Allocation Multiplicity: Evaluating the Promises of the Rashomon Set FAccT, 2040-2055. https://doi.org/10.1145/3715275.3732138 -
Homogeneous Algorithms Can Reduce Competition in Personalized Pricing
Citation: Nathanael Jo, Kathleen Creel, Ashia Wilson, Manish Raghavan. (2025). Homogeneous Algorithms Can Reduce Competition in Personalized Pricing CoRR, abs/2503.15634. https://doi.org/10.48550/arXiv.2503.15634 -
Ecosystem Graphs: Documenting the Foundation Model Supply Chain
Citation: Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, Percy Liang. (2024). Ecosystem Graphs: Documenting the Foundation Model Supply Chain AIES (1), 196-209. https://doi.org/10.1609/aies.v7i1.31629 -
Algorithmic Pluralism: A Structural Approach To Equal Opportunity
Citation: Shomik Jain, Vinith Suriyakumar, Kathleen Creel, Ashia Wilson. (2024). Algorithmic Pluralism: A Structural Approach To Equal Opportunity FAccT, 197-206. https://doi.org/10.1145/3630106.3658899 -
Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
Citation: Shomik Jain, Kathleen Creel, Ashia Wilson. (2024). Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized CoRR, abs/2404.08592. https://doi.org/10.48550/arXiv.2404.08592 -
Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes
Citation: Connor Toups, Rishi Bommasani, Kathleen A. Creel, Sarah H. Bana, Dan Jurafsky, Percy Liang. (2023). Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes CoRR, abs/2307.05862. https://doi.org/10.48550/arXiv.2307.05862 -
Ecosystem Graphs: The Social Footprint of Foundation Models
Citation: Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, Percy Liang. (2023). Ecosystem Graphs: The Social Footprint of Foundation Models CoRR, abs/2303.15772. https://doi.org/10.48550/arXiv.2303.15772 -
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?
Citation: Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang. (2022). Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? CoRR, abs/2211.13972. https://doi.org/10.48550/arXiv.2211.13972 -
Nifty Assignments
Citation: Nick Parlante, Julie Zelenski, Eric S. Roberts, Jed Rembold, Ben Stephenson, Jonathan Hudson, Stephanie Valentine, Juliette Woodrow, Kathleen Creel, Nick Bowman, Larry "Joshua" Crotts, Andrew Matzureff, Mike Izbicki. (2022). Nifty Assignments SIGCSE (2), 1067-1068. https://doi.org/10.1145/3478432.3499268 -
On the Opportunities and Risks of Foundation Models
Citation: Rishi Bommasani, Drew A. Hudson, Ehsan Adeli , Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei , Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson , John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, et al.. (2021). On the Opportunities and Risks of Foundation Models CoRR, abs/2108.07258. https://arxiv.org/abs/2108.07258 -
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems
Citation: Kathleen Creel, Deborah Hellman. (2021). The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems FAccT, 816. https://doi.org/10.1145/3442188.3445942
