在隐私受限下提升图神经网络公平性，无需完整人口信息
Fairness-Aware Graph Representation Learning with Limited Demographic Information
- 用部分人口数据生成代理信息，补全缺失标签
- 通过一致性嵌入策略降低不同群体间预测偏差
- 自适应调整节点贡献度，兼顾公平与模型性能
确保图神经网络的公平性是构建可信、负责任机器学习系统的关键。尽管近年来已提出多种公平图学习方法，但多数假设可完全访问人口信息，这在实践中极少满足，因隐私、法律或监管限制。为此，本文提出一种新型公平图学习框架 FairGLite，可在有限人口信息下缓解图学习中的偏见。具体而言，我们设计了一种基于部分人口数据的机制以生成人口信息代理，并提出一种策略，强制不同人口群体间的节点嵌入保持一致。此外，我们开发了一种自适应置信策略，根据预测置信度动态调整每个节点对公平性和效用的贡献。理论分析表明，该框架在群体公平性度量上实现了可证明的上界，提供形式化偏见缓解保证。在多个数据集和公平图学习框架上的大量实验验证了其在缓解偏见与维持模型性能方面的有效性。
原文摘要 · Abstract (English)
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning methods have been proposed in recent years. However, most of them assume full access to demographic information, a requirement rarely met in practice due to privacy, legal, or regulatory restrictions. To this end, this paper introduces a novel fair graph learning framework that mitigates bias in graph learning under limited demographic information. Specifically, we propose a mechanism guided by partial demographic data to generate proxies for demographic information and design a strategy that enforces consistent node embeddings across demographic groups. In addition, we develop an adaptive confidence strategy that dynamically adjusts each node's contribution to fairness and utility based on prediction confidence. We further provide theoretical analysis demonstrating that our framework, FairGLite, achieves provable upper bounds on group fairness metrics, offering formal guarantees for bias mitigation. Through extensive experiments on multiple datasets and fair graph learning frameworks, we demonstrate the framework's effectiveness in both mitigating bias and maintaining model utility.
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