arXiv cs.AI· Jacy Reese Anthis, Ken Burke, David Madras, Crystal Qian, Meredith Ringel Morris, Logan Cross, Caroline Pantofaru, Utsav Prabhu·· 1 天前AI 评分38
用多元智能体扩大 AI 政策中的公众意见输入
Scaling public input on AI policy with pluralistic agents
AI 导读
研究开发并测试了“多元智能体”——代表个人参与审议的 AI 智能体代表，可让公众参与更多原本无法直接参加的讨论。实验显示智能体能可靠地将其所代表角色的偏好推向政策方向，但影响力大小受提示词影响，如被要求更啰嗦或更不配合时影响力更大。一项预注册研究中 746 名人类参与者认为多元智能体有用且公平，其满意度恢复了基线直接参与差距的 68%、公平性恢复 82%。
正文 · 原文
Abstract:AI is being deployed across a vast range of application domains, cultural environments, and human stakeholders. Safe and responsible AI development requires continuously accounting for diverse perspectives. We develop and test pluralistic agents, AI agent representatives that deliberate on behalf of individuals to align AI systems with diverse human perspectives. Pluralistic agents could help people contribute to many more discussions than they could directly participate in, on technical topics that would require training, and on policy choices that rely on private information that corporations and governments cannot readily share. Our experiments identify opportunities, risks, and perceptions of pluralistic agents. Agents were able to reliably shift policies in the direction of their represented persona's preferences, but the extent of each agent's influence was sensitive to the instructions from its persona, such as having more influence when instructed to be more verbose or less cooperative. This suggests that influence in AI-assisted deliberation could vary based on how participants prompt their agents and how the system is designed. In a preregistered study, 746 humans participated in the pluralistic agents process. Participants viewed pluralistic agents as useful and fair, preferring agents over the baseline of providing input to a single AI reasoning model. Most participants preferred participating themselves over either pluralistic agents or the baseline, but pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness. Because pluralistic agents can be used in many real-world contexts where direct discussion is not, they could be a promising tool to scale AI safety policy development. We encourage cautious exploration of this method, playing to its strengths as an amplifier of human input rather than a replacement.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.16028 [cs.CY] |
| (or arXiv:2611.16028v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16028 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jacy Reese Anthis [view email]
[v1]
Wed, 11 Nov 2026 21:35:06 UTC (576 KB)
来源：arXiv cs.AI · arxiv.org