从网络不平等到网络公平：负责任决策的视角
From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making
- Complexity Science Hub Vienna（维也纳复杂性科学中心）
- Northeastern University（东北大学）
- Santa Fe Institute（圣塔菲研究所）
- Hong Kong Baptist University（香港浸会大学）
- Vienna University of Technology（维也纳工业大学）
- KTH Royal Institute of Technology（KTH皇家理工学院）
- Universitat Pompeu Fabra（庞培法布拉大学）
- Brown University（布朗大学）
- Graz University of Technology（格拉茨工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出网络化公平视角，识别十种网络效应，以学术招聘为例，结合分配与程序正义审视决策过程，呼吁超越静态类别应对结构性不平等。
AI中文摘要：
社交网络塑造了个人如何做出决策以及机会如何分配。然而，生成这些网络的机制往往反映了先前存在的不平等，而依赖网络衍生信号的技术则有可能进一步放大此类差异。算法公平性研究在很大程度上将网络视为固定背景，几乎完全基于分配正义进行分析，忽视了网络结构如何系统性地使决策产生偏差。在本视角文章中，我们识别了十种网络效应，并追溯它们如何在我们的测量意图与观察结果之间造成结构性偏差。以学术招聘为例，我们表明网络偏差本质上并非有害或有益。确定其合法性需要通过分配正义和程序正义的双重视角审视整个决策过程，同时让所有受影响的利益相关者参与其中。因此，我们呼吁采取一种整体的、网络化的公平方法，超越静态群体类别，并认识到不平等具有动态性、关系性和结构性。
英文摘要：
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.
