The usual definitions of algorithmic fairness focus on population-level statistics, such as demographic parity or equal opportunity. However, in many social or economic contexts, fairness is not perceived globally, but locally, through an individual's peer network and comparisons. We propose a theoretical model of perceived fairness networks, in which each individual's sense of discrimination depends on the local topology of interactions. We show that even if a decision rule satisfies standard criteria of fairness, perceived discrimination can persist or even increase in the presence of homophily or assortative mixing. We propose a formalism for the concept of fairness perception, linking network structure, local observation, and social perception. Analytical and simulation results highlight how network topology affects the divergence between objective fairness and perceived fairness, with implications for algorithmic governance and applications in finance and collaborative insurance.

翻译：传统的算法公平性定义通常关注群体层面的统计指标，例如人口统计均等或机会均等。然而，在许多社会或经济情境中，公平性并非被全局感知，而是通过个体的同侪网络和比较在局部被感知。我们提出了一个感知公平性网络的理论模型，其中个体的歧视感取决于交互的局部拓扑结构。我们证明，即使某个决策规则满足标准的公平性准则，在同质性或同类混合存在的情况下，感知到的歧视仍可能持续甚至加剧。我们为公平性感知的概念提出了一种形式化框架，将网络结构、局部观察与社会感知联系起来。分析和仿真结果揭示了网络拓扑如何影响客观公平性与感知公平性之间的差异，这对算法治理以及在金融和协作保险等领域的应用具有重要启示。