A Bayesian Framework for Human-AI Collaboration: Complementarity and Correlation Neglect
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917275673559040 |
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| author | Amin, Saurabh Bennouna, Amine Huttenlocher, Daniel Kong, Dingwen Lyu, Liang Ozdaglar, Asuman |
| author_facet | Amin, Saurabh Bennouna, Amine Huttenlocher, Daniel Kong, Dingwen Lyu, Liang Ozdaglar, Asuman |
| contents | We develop a decision-theoretic model of human-AI interaction to study when AI assistance improves or impairs human decision-making. A human decision-maker observes private information and receives a recommendation from an AI system, but may combine these signals imperfectly. We show that the effect of AI assistance decomposes into two main forces: the marginal informational value of the AI beyond what the human already knows, and a behavioral distortion arising from how the human uses the AI's recommendation. Central to our analysis is a micro-founded measure of informational overlap between human and AI knowledge. We study an empirically relevant form of imperfect decision-making -- correlation neglect -- whereby humans treat AI recommendations as independent of their own information despite shared evidence. Under this model, we characterize how overlap and AI capabilities shape the Human-AI interaction regime between augmentation, impairment, complementarity, and automation, and draw key insights. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_14331 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Bayesian Framework for Human-AI Collaboration: Complementarity and Correlation Neglect Amin, Saurabh Bennouna, Amine Huttenlocher, Daniel Kong, Dingwen Lyu, Liang Ozdaglar, Asuman Computer Science and Game Theory Human-Computer Interaction Theoretical Economics We develop a decision-theoretic model of human-AI interaction to study when AI assistance improves or impairs human decision-making. A human decision-maker observes private information and receives a recommendation from an AI system, but may combine these signals imperfectly. We show that the effect of AI assistance decomposes into two main forces: the marginal informational value of the AI beyond what the human already knows, and a behavioral distortion arising from how the human uses the AI's recommendation. Central to our analysis is a micro-founded measure of informational overlap between human and AI knowledge. We study an empirically relevant form of imperfect decision-making -- correlation neglect -- whereby humans treat AI recommendations as independent of their own information despite shared evidence. Under this model, we characterize how overlap and AI capabilities shape the Human-AI interaction regime between augmentation, impairment, complementarity, and automation, and draw key insights. |
| title | A Bayesian Framework for Human-AI Collaboration: Complementarity and Correlation Neglect |
| topic | Computer Science and Game Theory Human-Computer Interaction Theoretical Economics |
| url | https://arxiv.org/abs/2602.14331 |