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Found 7 papers, 2 papers with code
Evaluating the World Model Implicit in a Generative Model
Building generative models that meaningfully capture the underlying logic of the domains they model would be immensely valuable; our results suggest new ways to assess how close a given model is to that goal.
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
Our results show that -- especially for cases where the cost of mistakes is high -- more capable models (e. g. GPT-4) can do worse on the instances people choose to use them for, exactly because they are not aligned with the human generalization function.
From Predictive Algorithms to Automatic Generation of Anomalies
Facing a similar problem -- how to extract theoretical insights from their intuitions -- researchers often turned to ``anomalies:'' constructed examples that highlight flaws in an existing theory and spur the development of new ones.
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
Predictive algorithms inform consequential decisions in settings where the outcome is selectively observed given choices made by human decision makers.
Characterizing Fairness Over the Set of Good Models Under Selective Labels
We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance, or "the set of good models."
Design-Based Uncertainty for Quasi-Experiments
An interesting feature of our framework is that conventional standard errors tend to become more conservative when treatment probabilities vary more across units, i. e. when there is more selection into treatment.
