Perceived Fairness in Networks
For designers of algorithmic systems in social contexts, this work highlights a gap between global fairness metrics and local perceptions, though the analysis is theoretical and incremental.
The paper shows that standard algorithmic fairness metrics can fail to align with individuals' perceived fairness when decisions are observed through local peer networks, and that homophily can exacerbate this divergence.
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.