Kathleen Creel

According to our database, Kathleen Creel authored at least 10 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.

Shomik Jain, Kathleen Creel, Ashia Wilson

CoRR, 2024

Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.

Shomik Jain, Kathleen Creel, Ashia Camage Wilson

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Algorithmic Pluralism: A Structural Approach To Equal Opportunity.

Shomik Jain, Vinith M. Suriyakumar, Kathleen Creel, Ashia Wilson

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Ecosystem Graphs: The Social Footprint of Foundation Models.

Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, Percy Liang

CoRR, 2023

Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes.

Connor Toups, Rishi Bommasani, Kathleen Creel, Sarah H. Bana, Dan Jurafsky, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Nifty Assignments.

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

Proceedings of the SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education, 2022

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

On the Opportunities and Risks of Foundation Models.

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. 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 E. 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.

CoRR, 2021

The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems.

Kathleen Creel, Deborah Hellman

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

2019

What makes a good explanation? Cognitive dimensions of explaining intelligent machines.

Roberto Confalonieri, Tarek R. Besold, Tillman Weyde, Kathleen Creel, Tania Lombrozo, Shane T. Mueller, Patrick Shafto

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019