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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.16899 |
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| _version_ | 1866912387923181568 |
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| author | Oktar, Kerem Collins, Katherine M. Hernandez-Orallo, Jose Coyle, Diane Cave, Stephen Weller, Adrian Sucholutsky, Ilia |
| author_facet | Oktar, Kerem Collins, Katherine M. Hernandez-Orallo, Jose Coyle, Diane Cave, Stephen Weller, Adrian Sucholutsky, Ilia |
| contents | Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of collaboration and extended cognition, yet they also pose major risks-including and beyond risks of typical AI tools and agents. In this commentary, we systematically identify risks of AI thought partners through a novel framework that identifies risks at multiple levels of analysis, including Real-time, Individual, and Societal risks arising from collaborative cognition (RISc). We leverage this framework to propose concrete metrics for risk evaluation, and finally suggest specific mitigation strategies for developers and policymakers. As AI thought partners continue to proliferate, these strategies can help prevent major harms and ensure that humans actively benefit from productive thought partnerships. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships Oktar, Kerem Collins, Katherine M. Hernandez-Orallo, Jose Coyle, Diane Cave, Stephen Weller, Adrian Sucholutsky, Ilia Artificial Intelligence Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of collaboration and extended cognition, yet they also pose major risks-including and beyond risks of typical AI tools and agents. In this commentary, we systematically identify risks of AI thought partners through a novel framework that identifies risks at multiple levels of analysis, including Real-time, Individual, and Societal risks arising from collaborative cognition (RISc). We leverage this framework to propose concrete metrics for risk evaluation, and finally suggest specific mitigation strategies for developers and policymakers. As AI thought partners continue to proliferate, these strategies can help prevent major harms and ensure that humans actively benefit from productive thought partnerships. |
| title | Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.16899 |