8 citations · 15 across the 14 of their papers we have counts for
5 papers · 1 filter
Learning Checklists to Reduce Lottery Effects in Scientific Peer Review
Xuanyou Liu, Ziyang Guo, Jason Hartline +1
Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We…
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning
Dong Shu, Ziyang Guo, Negar Kamali +2
Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training a…
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
Negar Kamali, Candice Rockell Gerstner, Jessica Hullman +1
Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becom…
Hypothesizing an effect size by considering individual variation
Andrew Gelman, Amy Krefman, Lauren Kennedy +1
When designing and evaluating an experiment or observational study, it is useful to have a realistic hypothesis regarding the average treatment effect. We present an approach to co…
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
Jessica Hullman, David Broska, Huaman Sun +1
A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. Howeve…