Human-AI Collaboration with Misaligned Preferences
Jiaxin Song, Parnian Shahkar, Kate Donahue, and Bhaskar Ray Chaudhury. Under review.
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
Charlotte Park, Kate Donahue, Manish Raghavan. Preliminary version accepted at UMAP workshop on Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2025).
AI-Assisted Decision Making with Human Learning
Gali Noti, Kate Donahue, Jon Kleinberg, Sigal Oren. Accepted at EC (2025).
AI as a Resource: Strategy, Uncertainty, and Societal Welfare
Kate Donahue (PhD dissertation). August '24 (won a dissertation award from Cornell Computer Science).
Optimal Selection Using Algorithmic Rankings with Side Information
Kate Donahue, Nicole Immorlica, Brendan Lucier (alphabetical order). Preliminary versions to appear at Neurips '24 workshops on Regulatable ML and Algorithmic Fairness through the Lens of Time. Working version here, final version in preparation.
Private Blotto: Viewpoint Competition with Polarized Agents
Kate Donahue and Jon Kleinberg. Preliminary version accepted at EAAMO 2023. Full version accepted at AAAI 2025.
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins (alphabetical order). Accepted at ICML 2024. Presented at ESIF '24 (oral) and EC '24 (contributed poster).
An Abundance of Katherines: The Game Theory of Baby Naming
Katy Blumer, Kate Donahue, Katie Fritz, Kate Ivanovich, Katherine Lee, Katie Luo, Cathy Meng, Katie Van Koevering (alphabetical order). SIGBOVIK 2024. [Note: this is an April Fool's day paper]
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making
Kate Donahue, Sreenivas Gollapudi, Kostas Kollias. AAAI 2024 (oral presentation). Summary in the Montreal AI Ethics blog.
Models of fairness in federated learning (Model-Sharing Games III)
Kate Donahue and Jon Kleinberg, 2022. Oral presentation at Neurips workshop on Learning and Decision-making with Strategic Feedback and poster at EAAMO 2022. Accepted at The Web Conference 2023.
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Kate Donahue, Alexandra Chouldechova, Krishnaram Kenthapadi, 2021. Panel presentation at Neurips workshop on Human-Centered AI. Full version accepted at FAccT 2022. Summary in the Montreal AI Ethics blog.
Optimality and Stability in Federated Learning: A Game-theoretic Approach (Model-Sharing Games II)
Kate Donahue and Jon Kleinberg, 2021. Accepted at Neurips 2021.
Kate Donahue and Jon Kleinberg, 2021. Github repository here. Accepted at AAAI 2021.
Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness
Kate Donahue and Solon Barocas, 2021. NeurIPS Workshop on Consequential Decision Making in Dynamic Environments, contributed talk. Accepted at FAccT 2021.
Fairness and Utilization In Allocating Resources With Uncertain Demand
Kate Donahue and Jon Kleinberg, 2020. Mechanism Design for Social Good, 2019, FAccT 2020, where it won Best Paper in the CS category.
Evolving cooperation in multichannel games
Kate Donahue, Oliver P. Hauser, Martin A. Nowak, Christian Hilbe. Published in Nature Communications, August 2020.