From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making
Abstract
Social networks shape how individuals make decisions and how opportunities are distributed. However, the mechanisms that generate these networks often reflect pre-existing inequalities, and technologies that rely on network-derived signals risk further amplifying such disparities. Algorithmic fairness research largely treats networks as a fixed background, grounding analysis almost exclusively in distributive justice and overlooking how network structures systematically bias decision-making. In this Perspective, we identify ten network effects and trace how they create structural biases in the relationship between what we intend to measure and what we observe. Using academic hiring as an example, we show that network biases are not inherently harmful or beneficial. Determining their legitimacy requires examining the entire decision-making process through the lenses of both distributive and procedural justice while engaging all affected stakeholders. We therefore call for a holistic, networked approach to fairness that moves beyond static group categories and recognizes the dynamic, relational, and structural nature of inequality.
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BibTeX
@misc{espinnoboa2026from,
title = {{From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making}},
author = {Lisette Espín-Noboa and Tina Eliassi-Rad and Pak-Hang Wong and Erich Prem and Meike Zehlike and Ricardo Baeza-Yates and Suresh Venkatasubramanian and Fariba Karimi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13867},
eprint = {2609.13867},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13867},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making
AU - Lisette Espín-Noboa
AU - Tina Eliassi-Rad
AU - Pak-Hang Wong
AU - Erich Prem
AU - Meike Zehlike
AU - Ricardo Baeza-Yates
AU - Suresh Venkatasubramanian
AU - Fariba Karimi
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.13867
UR - https://arxiv.org/abs/2609.13867
AB - Social networks shape how individuals make decisions and how opportunities are distributed. However, the mechanisms that generate these networks often reflect pre-existing inequalities, and technologies that rely on network-derived signals risk further amplifying such disparities. Algorithmic fairness research largely treats networks as a fixed background, grounding analysis almost exclusively in distributive justice and overlooking how network structures systematically bias decision-making. In this Perspective, we identify ten network effects and trace how they create structural biases in the relationship between what we intend to measure and what we observe. Using academic hiring as an example, we show that network biases are not inherently harmful or beneficial. Determining their legitimacy requires examining the entire decision-making process through the lenses of both distributive and procedural justice while engaging all affected stakeholders. We therefore call for a holistic, networked approach to fairness that moves beyond static group categories and recognizes the dynamic, relational, and structural nature of inequality.
ER -