为公平的图形表示而进行脱而出的对比学习
Guixian Zhang1, Guan Yuan1, Debo Cheng2
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China; Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China; Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China.
概括
图形神经网络 (GNN) 可以进行歧视. 公平脱的图形神经网络 (FDGNN) 框架使用数据增强和脱的对比学习来创建公平的节点表示,防止人工智能的偏见. 这种方法通过保护弱势群体来确保可信的人工智能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 图形神经网络 (GNN) 对于从图形结构数据中学习至关重要.
- 由于敏感的属性,GNN预测可能会有偏见,导致歧视.
- 迫切需要方法来确保GNN应用程序的公平性.
研究的目的:
- 提出一个新的框架,公平解图形神经网络 (FDGNN),用于学习公平节点表示.
- 解决 GNN 中的算法歧视问题,保护弱势群体.
- 为建立更值得信赖的人工智能系统.
主要方法:
- 通过增强,FDGNN增强了数据多样性,创建具有相同灵敏度但不同的图形结构的实例.
- 反事实增强策略平衡了跨组的敏感属性分布.
- 不纠的对比学习将敏感与非敏感的属性分开来进行公平的预测.
主要成果:
- FDGNN在三个现实数据集的预测中表现出卓越的公平性.
- 该框架有效地学习了脱而出的表示,最大限度地降低了敏感信息的影响.
- 实验结果验证了与基线方法相比,FDGNN的疗效.
结论:
- 在图形神经网络中实现公平性,FDGNN提供了一个强大的解决方案.
- 解是一种有前途的技术,用于学习图形数据中的公平表示.
- 该框架有助于开发值得信赖和公平的AI.
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