为公平的图形神经网络迁移人口群
YanMing Hu1, TianChi Liao2, JiaLong Chen1
1School of Computer Science and Engineering, Sun Yat-sen University, GuangZhou, China.
概括
公平迁移动态调整人口群体以提高图形神经网络 (GNN) 的公平性. 这种新的框架提高了模型性能,同时减轻了图形学习应用中的偏差.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图形神经网络 (GNN) 在图形学习任务中表现出卓越的性能.
- 在GNN中现有的公平技术通常依赖于固定的敏感属性,未能解决潜在的偏见.
- 培训数据中的偏见信息可能会导致特定人口群体的不公平结果.
研究的目的:
- 为了解决现有的公平GNN技术的局限性.
- 为动态的人口群组调整提出一个新的框架,FairMigration.
- 改善模型性能与GNN中的公平性之间的权衡.
主要方法:
- 公平移民采用了两阶段的培训过程.
- 第1阶段:初始GNN优化与个性化的自我监督学习和动态的人口群组调整.
- 第二阶段:与结的人口群体进行监督学习,包括对抗训练.
主要成果:
- 公平移民有效地动态地迁移人口群体.
- 该框架在模型性能和公平性之间实现了有利的平衡.
- 广泛的实验验证了拟议方法的有效性.
结论:
- 公平移民为实现 GNN 公平提供了一个新的范式.
- 人口群体的动态调整对于减轻偏见至关重要.
- 该框架显示了开发更公平的人工智能系统的前景.
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