AI Behavioral Science
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911730179768320 |
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| author | Jackson, Matthew O. Me, Qiaozhu Wang, Stephanie W. Xie, Yutong Yuan, Walter Benzell, Seth Brynjolfsson, Erik Camerer, Colin F. Evans, James Jabarian, Brian Kleinberg, Jon Meng, Juanjuan Mullainathan, Sendhil Ozdaglar, Asuman Pfeiffer, Thomas Tennenholtz, Moshe Willer, Robb Yang, Diyi Ye, Teng |
| author_facet | Jackson, Matthew O. Me, Qiaozhu Wang, Stephanie W. Xie, Yutong Yuan, Walter Benzell, Seth Brynjolfsson, Erik Camerer, Colin F. Evans, James Jabarian, Brian Kleinberg, Jon Meng, Juanjuan Mullainathan, Sendhil Ozdaglar, Asuman Pfeiffer, Thomas Tennenholtz, Moshe Willer, Robb Yang, Diyi Ye, Teng |
| contents | We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Behavioral Science Jackson, Matthew O. Me, Qiaozhu Wang, Stephanie W. Xie, Yutong Yuan, Walter Benzell, Seth Brynjolfsson, Erik Camerer, Colin F. Evans, James Jabarian, Brian Kleinberg, Jon Meng, Juanjuan Mullainathan, Sendhil Ozdaglar, Asuman Pfeiffer, Thomas Tennenholtz, Moshe Willer, Robb Yang, Diyi Ye, Teng Human-Computer Interaction General Economics Economics We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it becomes vital to develop techniques for assessing AI behavior. We outline how tools developed to assess people's behaviors by social scientists can be used to assess and infer AI's behaviors biases, tendencies, and heuristics. Second, we also discuss how AI can change the ways in which we learn about human behavior. Beyond its computational power, AI offers new techniques for simulating, inferring, and predicting human behaviors that we outline and discuss. Third, as humans and AI are interacting in increasingly complex and intertwined systems, we need to understand the implications for the resulting economic and political outcomes. We outline issues that are increasingly pressing concerning the future of human-AI interactions and potential changes and disruptions that can ensue. |
| title | AI Behavioral Science |
| topic | Human-Computer Interaction General Economics Economics |
| url | https://arxiv.org/abs/2509.13323 |