梳理多智能体AI公平性研究现状，指出其缺乏系统性与人性考量。
Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems
- 分析23篇论文，归纳出五类典型公平性研究范式。
- 发现现有研究多流于表面，忽视智能体自主性带来的复杂影响。
- 呼吁将公平性嵌入开发全周期，需明确目标与人类监督。
生成式AI的快速发展催生了日益复杂的多智能体AI（MAAI）系统。尽管传统预测场景中的AI公平性已有广泛研究，但在MAAI领域仍处于起步阶段且分散。本范围综述通过定性内容分析23项研究，提炼出五种典型方法。结果表明，当前对MAAI公平性的探讨往往浮于表面，缺乏坚实的规范基础，常忽略智能体自主性与系统级互动带来的复杂动态。我们主张，公平性应贯穿MAAI开发全生命周期，而非事后附加。有意义的评估需要明确的人类监督、规范清晰性以及对公平性目标与受益对象的精确界定。本综述为推进MAAI公平性研究提供了基础，揭示关键空白，暴露现存局限，并提出未来方向。
原文摘要 · Abstract (English)
Rapid advances in Generative AI are giving rise to increasingly sophisticated Multi-Agent AI (MAAI) systems. While AI fairness has been extensively studied in traditional predictive scenarios, its examination in MAAI remains nascent and fragmented. This scoping review critically synthesizes existing research on fairness in MAAI systems. Through a qualitative content analysis of 23 selected studies, we identify five archetypal approaches. Our findings reveal that fairness in MAAI systems is often addressed superficially, lacks robust normative foundations, and frequently overlooks the complex dynamics introduced by agent autonomy and system-level interactions. We argue that fairness must be embedded structurally throughout the development lifecycle of MAAI, rather than appended as a post-hoc consideration. Meaningful evaluation requires explicit human oversight, normative clarity, and a precise articulation of fairness objectives and beneficiaries. This review provides a foundation for advancing fairness research in MAAI systems by highlighting critical gaps, exposing prevailing limitations, and suggesting pathways.
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