提出动态公平框架，让多智能体系统更公正透明
Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System
- 将公平视为智能体交互中涌现的动态属性
- 实证显示引入公平约束可提升决策公平性
- 适合关注AI伦理与系统设计融合的研究者
在去中心化多智能体系统中，由于涌现偏差、系统低效及代理间激励冲突，确保公平性面临重大挑战。本文对多智能体人工智能中的公平性进行了全面综述，提出一种新框架，将公平视为智能体交互中动态涌现的属性。该框架整合了公平约束、偏见缓解策略和激励机制，使自主代理行为与社会价值对齐，同时兼顾效率与鲁棒性。通过实证验证，我们证明引入公平约束能实现更公平的决策。本工作弥合了人工智能伦理与系统设计之间的鸿沟，为可问责、透明且具有社会责任感的多智能体系统提供了基础。
原文摘要 · Abstract (English)
Ensuring fairness in decentralized multi-agent systems presents significant challenges due to emergent biases, systemic inefficiencies, and conflicting agent incentives. This paper provides a comprehensive survey of fairness in multi-agent AI, introducing a novel framework where fairness is treated as a dynamic, emergent property of agent interactions. The framework integrates fairness constraints, bias mitigation strategies, and incentive mechanisms to align autonomous agent behaviors with societal values while balancing efficiency and robustness. Through empirical validation, we demonstrate that incorporating fairness constraints results in more equitable decision-making. This work bridges the gap between AI ethics and system design, offering a foundation for accountable, transparent, and socially responsible multi-agent AI systems.
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