Social networks shape fairness in decision making and opportunity

From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making

Social and Information NetworksComputers and SocietyMachine Learning

Summary

Social networks influence how people get opportunities and make choices, but these networks can reflect unfair advantages or disadvantages already present in society. The authors identify ten ways network structures can create biases between what decision-makers want to measure and what they actually observe. Using academic hiring as an example, they explain that these biases are not always good or bad; understanding fairness requires looking at the whole decision process and involving everyone affected. The paper argues for a broader view of fairness that considers how relationships and network structures cause inequality.

What this means in practice

  • For human resources teams: Improve fairness in hiring processes by accounting for network-related biases affecting candidate evaluations and opportunities.
  • For social platform designers: Design social networking features that mitigate structural biases and unfair amplifications in recommendations or connections.

A position paper. It proposes an approach and reports no results.

Authors

Lisette Espín-Noboa, Tina Eliassi-Rad, Pak-Hang Wong, Erich Prem, Meike Zehlike, Ricardo Baeza-Yates, Suresh Venkatasubramanian, Fariba Karimi

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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