Fairness Perception from a Network-Centric Perspective
Fairness Perception from a Network-Centric Perspective
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DOI：
10.1109/icdm50108.2020.00145
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Farzan Masrour;P. Tan;A. Esfahanian
中科院分区：
文献类型：
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作者：
Farzan Masrour;P. Tan;A. Esfahanian
近年来，随着机器学习算法的影响越来越广泛，数学公平性是一个主要问题。在本文中，我们从网络为中心的角度研究算法公平性问题。具体来说，我们引入了一种新颖而直观的功能称为公平感知，并提供了一个公理化的方法来分析其属性。使用同行评审网络作为案例研究，我们还研究了其效用评估的公平性的看法，在论文接受的决定。我们展示了如何将该函数扩展到一个组公平性度量，称为公平性可见性，并展示了其与人口统计学平价的关系。我们还讨论了一个潜在的陷阱的公平可见性措施，可以利用误导个人认为算法的决定是公平的。我们演示了如何通过增加公平感知函数的局部邻域大小来缓解这个问题。
Algorithmic fairness is a major concern in recent years as the influence of machine learning algorithms becomes more widespread. In this paper, we investigate the issue of algorithmic fairness from a network-centric perspective. Specifically, we introduce a novel yet intuitive function known as fairness perception and provide an axiomatic approach to analyze its properties. Using a peer-review network as a case study, we also examine its utility in terms of assessing the perception of fairness in paper acceptance decisions. We show how the function can be extended to a group fairness metric known as fairness visibility and demonstrate its relationship to demographic parity. We also discuss a potential pitfall of the fairness visibility measure that can be exploited to mislead individuals into perceiving that the algorithmic decisions are fair. We demonstrate how the problem can be alleviated by increasing the local neighborhood size of the fairness perception function.