A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , articleno =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
A user study finds that LLM reasoning traces and post-hoc explanations create false trust by increasing acceptance of incorrect answers, whereas contrastive dual explanations improve users' ability to detect errors.
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.
citing papers explorer
-
Instance-Adaptive Online Multicalibration
A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.
-
What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
-
Evaluating the False Trust engendered by LLM Explanations
A user study finds that LLM reasoning traces and post-hoc explanations create false trust by increasing acceptance of incorrect answers, whereas contrastive dual explanations improve users' ability to detect errors.
-
From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants
Structural mental models of AI writing assistants improve system understanding and usability but result in more grammatical errors in user writing compared to functional models.