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A Statistical Framework for Measuring AI Reliance
We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions.
Decision Theoretic Foundations for Experiments Evaluating Human Decisions
We argue that to attribute loss in human performance to forms of bias, an experiment must provide participants with the information that a rational agent would need to identify the normative decision.
Fair Grading Algorithms for Randomized Exams
In a randomized exam, each student is asked a small number of random questions from a large question bank.
Classification Protocols with Minimal Disclosure
We consider multi-party protocols for classification that are motivated by applications such as e-discovery in court proceedings.
No-Regret Learning in Bayesian Games
Recent price-of-anarchy analyses of games of complete information suggest that coarse correlated equilibria, which characterize outcomes resulting from no-regret learning dynamics, have near-optimal welfare.
