Trade less Accuracy for Fairness and Trade-off Explanation for GNN
Trade less Accuracy for Fairness and Trade-off Explanation for GNN
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DOI：
10.1109/bigdata55660.2022.10020318
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Yazheng Liu;Xi Zhang;Sihong Xie
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文献类型：
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作者：
Yazheng Liu;Xi Zhang;Sihong Xie
图在社交网络分析和电子商务中广泛存在，其中图神经网络（GNN）是最先进的模型。由于敏感属性和网络拓扑结构，GNN可能会有偏差。通过学习公平节点表示或邻接矩阵的现有工作，在保持预测准确性的同时实现组公平性的强有力保证仍然具有挑战性，公平性-准确性权衡对人类决策者来说仍然是模糊的。我们首先定义并分析了一个新的组公平性上界，以优化邻接矩阵的公平性，而不会显着损害预测精度。为了理解公平性和准确性权衡的细微差别，我们进一步提出了宏观和微观的解释方法，以揭示权衡和可以利用的空间。宏观解释方法是基于分层抽样和线性规划来确定性地解释群体公平性和预测准确性的动态变化。深入到微观层面，我们提出了一个基于路径的解释，揭示了网络拓扑如何导致权衡。在七个图形数据集上，我们证明了新的上界可以实现更有效的公平性-准确性权衡，并且解释方法的直观性可以清楚地指出权衡的改进。
Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved.