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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/2507.03409 |
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| _version_ | 1866909675765628928 |
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| author | Summerfield, Christopher Luettgau, Lennart Dubois, Magda Kirk, Hannah Rose Hackenburg, Kobi Fist, Catherine Slama, Katarina Ding, Nicola Anselmetti, Rebecca Strait, Andrew Giulianelli, Mario Ududec, Cozmin |
| author_facet | Summerfield, Christopher Luettgau, Lennart Dubois, Magda Kirk, Hannah Rose Hackenburg, Kobi Fist, Catherine Slama, Katarina Ding, Nicola Anselmetti, Rebecca Strait, Andrew Giulianelli, Mario Ududec, Cozmin |
| contents | We examine recent research that asks whether current AI systems may be developing a capacity for "scheming" (covertly and strategically pursuing misaligned goals). We compare current research practices in this field to those adopted in the 1970s to test whether non-human primates could master natural language. We argue that there are lessons to be learned from that historical research endeavour, which was characterised by an overattribution of human traits to other agents, an excessive reliance on anecdote and descriptive analysis, and a failure to articulate a strong theoretical framework for the research. We recommend that research into AI scheming actively seeks to avoid these pitfalls. We outline some concrete steps that can be taken for this research programme to advance in a productive and scientifically rigorous fashion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language Summerfield, Christopher Luettgau, Lennart Dubois, Magda Kirk, Hannah Rose Hackenburg, Kobi Fist, Catherine Slama, Katarina Ding, Nicola Anselmetti, Rebecca Strait, Andrew Giulianelli, Mario Ududec, Cozmin Artificial Intelligence We examine recent research that asks whether current AI systems may be developing a capacity for "scheming" (covertly and strategically pursuing misaligned goals). We compare current research practices in this field to those adopted in the 1970s to test whether non-human primates could master natural language. We argue that there are lessons to be learned from that historical research endeavour, which was characterised by an overattribution of human traits to other agents, an excessive reliance on anecdote and descriptive analysis, and a failure to articulate a strong theoretical framework for the research. We recommend that research into AI scheming actively seeks to avoid these pitfalls. We outline some concrete steps that can be taken for this research programme to advance in a productive and scientifically rigorous fashion. |
| title | Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.03409 |