On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Peizhao Li;Yifei Wang;Han Zhao;Pengyu Hong;Hongfu Liu
中科院分区：
文献类型：
--
作者：
Peizhao Li;Yifei Wang;Han Zhao;Pengyu Hong;Hongfu Liu
不同的影响已经引起了对机器学习应用及其社会影响的严重关注。为了满足减轻歧视的需要，公平性被视为算法设计中的一个重要属性。在这项工作中，我们研究了图结构数据的不同影响问题。具体地说，我们关注并元公平性，它阐明了一个公平性概念，即两个实例之间的预测关系应该独立于敏感属性。在此基础上，我们在理论上将图连接与学习图神经网络中链路预测分数的并矢公平性联系起来，并揭示了调整图中现有边上的权重有条件地促进并矢公平性。随后，我们提出了我们的算法FairAdj，该算法通过经验学习具有适当图结构约束的公平邻接矩阵来进行公平链接预测，同时尽可能地保持预测精度。实验验证表明，我们的方法在各种统计量方面提供了有效的并矢公平性，同时享受了良好的公平性和效益性折衷。
Disparate impact has raised serious concerns in machine learning applications and its societal impacts. In response to the need of mitigating discrimination, fairness has been regarded as a crucial property in algorithmic design. In this work, we study the problem of disparate impact on graph-structured data. Specifically, we focus on dyadic fairness, which articulates a fairness concept that a predictive relationship between two instances should be independent of the sensitive attributes. Based on this, we theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks, and reveal that regulating weights on existing edges in a graph contributes to dyadic fairness conditionally. Subsequently, we propose our algorithm, FairAdj, to empirically learn a fair adjacency matrix with proper graph structural constraints for fair link prediction, and in the meanwhile preserve predictive accuracy as much as possible. Empirical validation demonstrates that our method delivers effective dyadic fairness in terms of various statistics, and at the same time enjoys a favorable fairness-utility tradeoff.