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Beyond Accuracy: How AI Metacognitive Sensitivity improves AI-assisted Decision Making
In settings where human decision‐making relies on AI input, both the predictive accuracy of the AI system and the reliability of its confidence estimates influence decision quality. We highlight the role of AI metacognitive sensitivity---its ability to assign confidence scores that accurately distinguish correct from incorrect predictions---and introduce a theoretical framework for assessing the joint impact of AI's predictive accuracy and metacognitive sensitivity in hybrid decision‐making settings. Our research identifies conditions under which an AI with lower predictive accuracy but higher metacognitive sensitivity can enhance the overall accuracy of human decision making. Finally, a behavioral experiment confirms that greater AI metacognitive sensitivity improves human decision performance. Together, these findings underscore the importance of evaluating AI assistance not only by accuracy but also by metacognitive sensitivity, and of optimizing both to achieve superior decision outcomes.

Walking the Line: Balancing AI Advice and Human Annoyance for Human-AI Complementarity
Human-AI complementarity requires the performance of a human-AI team to be better than either agent type alone. One practical hurdle for achieving human-AI complementarity is that people can get annoyed with AI assistance, leading them to turn it off. What should an AI assistant do, if it knows that a less than enthusiastically received suggestion might prompt the human to shut it off for good? To get a handle on this question, we designed an experiment in which people have access to a deliberately annoying AI assistant and find that people are not very good at judging when they ought (not) to use our AI helper. This reveals a fundamental challenge: if people struggle to assess whether AI assistance could benefit them at any given time, then it might be better for the assistant to offer help rather than wait for the human to request it. But then again, an assistant that speaks up is annoying and so might not get to help for long. To solve this tricky balancing act, we propose a POMDP framework that integrates a cognitive model of human annoyance decisions to figure out when speaking up is worth the risk. In principle, this method could address both human over- and under-reliance on AI.

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