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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/2506.17434 |
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| _version_ | 1866910052158275584 |
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| author | Levine, Sydney Franklin, Matija Zhi-Xuan, Tan Guyot, Secil Yanik Wong, Lionel Kilov, Daniel Choi, Yejin Tenenbaum, Joshua B. Goodman, Noah Lazar, Seth Gabriel, Iason |
| author_facet | Levine, Sydney Franklin, Matija Zhi-Xuan, Tan Guyot, Secil Yanik Wong, Lionel Kilov, Daniel Choi, Yejin Tenenbaum, Joshua B. Goodman, Noah Lazar, Seth Gabriel, Iason |
| contents | AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes grounding those decisions in agreements that diverse stakeholders would endorse under the right conditions, yet securing such agreement at scale remains costly and slow -- even for advanced AI. We therefore propose Resource-Rational Contractualism (RRC): a framework where AI systems approximate the agreements rational parties would form by drawing on a toolbox of normatively-grounded, cognitively-inspired heuristics that trade effort for accuracy. An RRC-aligned agent would not only operate efficiently, but also be equipped to dynamically adapt to and interpret the ever-changing human social world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resource Rational Contractualism Should Guide AI Alignment Levine, Sydney Franklin, Matija Zhi-Xuan, Tan Guyot, Secil Yanik Wong, Lionel Kilov, Daniel Choi, Yejin Tenenbaum, Joshua B. Goodman, Noah Lazar, Seth Gabriel, Iason Artificial Intelligence AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes grounding those decisions in agreements that diverse stakeholders would endorse under the right conditions, yet securing such agreement at scale remains costly and slow -- even for advanced AI. We therefore propose Resource-Rational Contractualism (RRC): a framework where AI systems approximate the agreements rational parties would form by drawing on a toolbox of normatively-grounded, cognitively-inspired heuristics that trade effort for accuracy. An RRC-aligned agent would not only operate efficiently, but also be equipped to dynamically adapt to and interpret the ever-changing human social world. |
| title | Resource Rational Contractualism Should Guide AI Alignment |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17434 |