研究多智能体系统中偏见如何被放大，揭示系统级不公平的潜在风险。
Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems
- 通过提示诱导智能体产生群体偏好偏见，观察其对系统的影响。
- 发现系统整体偏见会超过个体偏见之和，出现非线性放大。
- 提出FBS度量方法，可量化偏见增强与压制的贡献比例，适合公平性研究者。
多智能体系统在支持各类任务时，通过智能体间的交互实现个人与集体目标。尽管能提升任务表现与决策能力，但通过减少偏见来保障公平性仍具挑战。本研究考察了个体智能体偏见如何演变并影响系统级公平性。我们使用提示使个体智能体暴露于群体偏好偏见中，随后评估其对系统层面的下游影响。为量化该影响，提出零中心度量指标Favor Bias Strength（FBS），可分解偏见变化中对优待群体的提升与受歧视群体的压制。基于多种智能体设计、基准测试及前沿大语言模型，结果表明，带有偏见的智能体显著影响系统整体公平性。有趣的是，当智能体均被均匀引入偏见时，系统级偏见反而上升，甚至超过个体偏见之和。实证结果凸显多智能体系统公平性的重要性和进一步分析的必要性。
原文摘要 · Abstract (English)
Multi-agent systems are increasingly deployed to support various tasks where agents interact to achieve individual and collective objectives. Although these systems can enhance task performance and decision-making, fairness preservation through bias reduction remains challenging. This study examines how agent-level biases shift and impact system-wide fairness. We use prompts to expose individual agents to group-favoring bias, then assess downstream impacts at the system level. To quantify the impact, we propose Favor Bias Strength (FBS), a zero-centered metric that decomposes bias alteration between favored-group uplift and disfavored-group suppression. Using multiple agent designs, benchmarks, and up-to-date large language models, we show that agents endowed with bias can substantially affect system-wide fairness. Interestingly, when agents are exposed to bias uniformly, the system-wide bias elevates, even exceeding the additive sum of the individual agents' biases. The empirical evidence underscores the criticality of fairness in multi-agent systems, which warrants further analyses and empirical tests.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。