为多智能体系统引入受保护属性，评估并避免因身份特征导致的奖励不公。
Using Protected Attributes to Consider Fairness in Multi-Agent Systems
- 将算法公平性中的受保护属性概念引入多智能体系统，定义不应影响预期回报的特征。
- 适配三种公平性度量：群体平等、反事实公平与条件统计平等，用于评估系统公平性。
- 适用于关注社会公平的博弈、资源分配等多智能体场景，尤其适合设计包容性系统。
多智能体系统（MAS）中的公平性已被广泛研究，特别是在商品分配、资源划分、抽奖和谈判系统等场景中代理间的奖励分配问题。公平性受系统规则、代理行为及其特征等多种因素影响。然而，人类社会中的公平常涉及对弱势与优势群体间差异的评估，遵循平等、多元与包容（EDI）原则。受算法公平性研究启发，我们定义多智能体系统的受保护属性为那些不应使代理在预期回报上处于不利地位的特征。我们从算法公平性文献中适配了三种度量方法——群体平等、反事实公平与条件统计平等——应用于多智能体环境，其中自利代理在环境中互动。这些度量使我们能够评估多智能体系统的公平性，最终目标是设计出不因受保护属性而歧视代理的系统。
原文摘要 · Abstract (English)
Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotteries, and bargaining systems. Fairness in MAS depends on various factors, including the system's governing rules, the behaviour of the agents, and their characteristics. Yet, fairness in human society often involves evaluating disparities between disadvantaged and privileged groups, guided by principles of Equality, Diversity, and Inclusion (EDI). Taking inspiration from the work on algorithmic fairness, which addresses bias in machine learning-based decision-making, we define protected attributes for MAS as characteristics that should not disadvantage an agent in terms of its expected rewards. We adapt fairness metrics from the algorithmic fairness literature -- namely, demographic parity, counterfactual fairness, and conditional statistical parity -- to the multi-agent setting, where self-interested agents interact within an environment. These metrics allow us to evaluate the fairness of MAS, with the ultimate aim of designing MAS that do not disadvantage agents based on protected attributes.
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