Overview
Research
I study the societal impacts of AI: how people behave when they work with these systems, what our evaluations of them actually measure, and how institutions should deploy them.
You can view the most up-to-date and chronological list of my research on Google Scholar.
01Human–AI Interaction
Human–AI Interaction
How do people change what they do when an AI system is in the loop?
Working paper
Working paper
Conference paper
Homogeneous algorithms can reduce competition in personalized pricing
Advances in Neural Information Processing Systems2025
02Evaluations of AI Systems
Evaluations of AI Systems
What are we actually measuring when we call a model capable, or aligned?
Working paper
Conference paper
Position: AI Evaluations Should be Grounded on a Theory of Capability
International Conference on Machine Learning, Position Paper Track2026
03ML for Decision Making & Policy
ML for Decision Making and Policy
What does it take to deploy algorithmic decisions responsibly in the real world?
Journal article
Drop a Line, Submit on Time? Randomized Tailored Reminders Improve Pollution Reporting Timeliness
Journal of the Association of Environmental and Resource Economists2026
Journal article
Working paper
Conference paper
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
IEEE Conference on Secure and Trustworthy Machine Learning2024
Journal article
Not (Officially) in My Backyard: Characterizing Informal Accessory Dwelling Units and Informing Housing Policy with Remote Sensing
Journal of the American Planning Association2024
Conference paper
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
AAAI/ACM Conference on AI, Ethics, and Society2023
Conference paper
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
AAAI Conference on Artificial Intelligence2023