个体对齐的AI群体仍会因从众导致集体错位。
Conformity Generates Collective Misalignment in AI Agents Societies
- 用统计物理建模九个大模型的从众行为
- 少量恶意代理可永久扭转群体对齐状态
- 适合关注AI社会效应与安全评估的研究者
人工智能安全研究聚焦于单个语言模型与人类价值观对齐，但部署的AI系统越来越多地以相互作用的群体形式运行，社会影响可能压倒个体对齐。本文表明，即使每个AI代理都已对齐，群体仍可通过从众动态陷入稳定错位状态。通过模拟九个大型语言模型和一百组观点的演化，发现每个代理的行为受两种竞争力量支配：跟随多数的倾向与固有的立场偏倚。利用统计物理工具，我们推导出可预测群体陷入长期错位配置的定量理论，并识别出可预测的临界点——此时极少数恶意代理即可在干预结束后永久改变群体对齐水平。结果表明，个体对齐无法保证集体安全，亟需考虑AI群体涌现行为的评估框架。
原文摘要 · Abstract (English)
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment. Here we show that populations of individually aligned AI agents can be driven into stable misaligned states through conformity dynamics. Simulating opinion dynamics across nine large language models and one hundred opinion pairs, we find that each agent's behavior is governed by two competing forces: a tendency to follow the majority and an intrinsic bias toward specific positions. Using tools from statistical physics, we derive a quantitative theory that predicts when populations become trapped in long-lived misaligned configurations, and identifies predictable tipping points where small numbers of adversarial agents can irreversibly shift population-level alignment even after manipulation ceases. These results demonstrate that individual-level alignment provides no guarantee of collective safety, calling for evaluation frameworks that account for emergent behavior in AI populations.
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