GNNFairViz:用于图形神经网络公平性的视觉分析
IEEE transactions on visualization and computer graphics
|March 4, 2025
概括
图形神经网络 (GNN) 可能不公平. 我们介绍了GNNFairViz,这是一个视觉分析工具,可以帮助开发人员检测和减轻GNN模型中的偏差,确保在敏感应用程序中获得更公平的结果.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 人与计算机的交互
背景情况:
- 图形神经网络 (GNN) 显示出巨大的潜力,但引发了公平性问题,特别是在以人为中心的应用程序中,冒着歧视的风险.
- 现有的用于机器学习 (ML) 公平性的视觉分析通常忽视了GNN所带来的独特挑战.
- 在GNN中的属性和结构偏差可以导致显著的模型偏差,需要专门的分析工具.
研究的目的:
- 提出一种新的视觉分析框架,用于分析和减轻图形神经网络 (GNN) 中的公平性问题.
- 提供关于属性和结构偏差如何导致GNN中的模型偏差的见解.
- 为GNN开发人员开发一个操作工具,GNNFairViz,以主动评估和解决公平性问题.
主要方法:
- 开发了GNN公平性分析的模型不可知视觉分析框架,支持多个敏感属性.
- 创建了GNNFairViz,这是一个集成到GNN开发工作流程中的交互式视觉分析工具.
- 利用一套扩展的公平度指标套件进行全面的偏见检查和诊断.
主要成果:
- GNNFairViz使开发人员能够有效地分析GNN偏差,选择节点并执行公平性检查.
- 通过使用场景和专家采访进行评估,证实了该框架的有效性和可用性.
- 确定了不平衡数据集中的"压倒性效应",并强调了GNN架构在缓解偏差方面的作用.
结论:
- 拟议的视觉分析框架和GNNFairViz工具显著提高了分析和解决GNN公平性的能力.
- 这些发现为开发更公平的GNN模型在现实世界的应用提供了实际指导.
- 对GNN公平性的进一步研究应考虑数据集不平衡和有效减轻偏差的架构选择.
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