Perceived Fairness in Networks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909988266442752 |
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| author | Charpentier, Arthur |
| author_facet | Charpentier, Arthur |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_12028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perceived Fairness in Networks Charpentier, Arthur Theoretical Economics Computer Science and Game Theory 90B15, 91D30 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. |
| title | Perceived Fairness in Networks |
| topic | Theoretical Economics Computer Science and Game Theory 90B15, 91D30 |
| url | https://arxiv.org/abs/2510.12028 |