Summary of Fair Graph Representation Learning Via Sensitive Attribute Disentanglement, by Yuchang Zhu et al.
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
by Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen
First submitted to arxiv on: 11 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Group fairness for Graph Neural Networks (GNNs) has become a crucial consideration, as it ensures that algorithmic decisions are independent of sensitive attributes like race and gender. Traditional approaches eliminate sensitive attribute information to achieve group fairness, but this can sacrifice task-related utility. To address this challenge, we propose FairSAD, a framework that enhances GNN fairness while preserving task-related information. Instead of eliminating sensitive attributes, FairSAD separates their related information into an independent component using Sensitive Attribute Disentanglement (SAD). Additionally, it utilizes a channel masking mechanism to identify and decorrelate the sensitive attribute-related component. Our experiments on real-world datasets demonstrate that FairSAD outperforms state-of-the-art methods in both fairness and utility performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Group fairness for Graph Neural Networks is important because it ensures algorithmic decisions don’t favor or harm certain groups based on sensitive attributes like race and gender. Most approaches eliminate sensitive attribute information to achieve group fairness, but this can sacrifice task-related utility. A new approach called FairSAD aims to improve GNN fairness while preserving task-related information. It does this by separating sensitive attribute-related information into an independent component using a process called Sensitive Attribute Disentanglement. This helps minimize the impact of sensitive attributes on GNN outcomes. |
Keywords
» Artificial intelligence » Gnn
