Perceived Fairness in Networks

Last week, the paper Perceived fairness in networks was published in Network Science.

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.

Arthur Charpentier
Arthur Charpentier
Arthur Charpentier, professor in Montréal, in Actuarial Science. Former professor-assistant at ENSAE Paristech, associate professor at Ecole Polytechnique and assistant professor… Read more

OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (June 3, 2026). Perceived Fairness in Networks. Freakonometrics. Retrieved June 8, 2026 from https://doi.org/10.58079/16bro


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