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

2026-09-12

AI Summary

本文探讨了社交网络中的不平等问题，通过识别十种网络效应及其在决策过程中的系统性偏见，呼吁采用整体、动态的方法来实现公平。

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.

Problem

• Network Inequality
• Algorithmic Fairness
• Structural Biases

Innovation

• Network Fairness
• Distributive Justice
• Procedural Justice
• Structural Biases
• Stakeholder Engagement