Summary of Towards Fair Graph Neural Networks Via Graph Counterfactual Without Sensitive Attributes, by Xuemin Wang et al.
Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
by Xuemin Wang, Tianlong Gu, Xuguang Bao, Liang Chang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed framework, Fairwos, aims to develop fair Graph Neural Networks (GNNs) for critical applications without requiring sensitive attributes. The existing statistical fairness notions may be insufficient when dealing with statistical anomalies, motivating the use of graph counterfactuals. To generate pseudo-sensitive attributes and find graph counterfactuals from real datasets, a mechanism is proposed to remedy the problem of missing sensitive attributes. A strategy is designed to ensure consistency between original data embeddings and those from graph counterfactuals, dynamically adjusting pseudo-sensitive attribute weights to balance fairness and utility. Theoretical demonstrations show that minimizing pseudo-sensitive attribute relations with predictions enables GNN fairness. Experimental results on six real-world datasets demonstrate Fairwos outperforms state-of-the-art methods in balancing utility and fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fairwos is a new approach to making Graph Neural Networks fair without having sensitive information. Right now, most fair GNNs focus on statistical fairness, which might not be enough when there are unusual patterns. To fix this, researchers looked at causal theory and came up with the idea of using graph counterfactuals to address unfairness. However, current methods need the sensitive attribute, but in many real-world situations, it’s hard or impossible to get that information due to privacy concerns. This makes existing methods useless. Fairwos proposes a new way to generate fake sensitive attributes and find these counterfactuals using real data. It also has a method to train fair GNNs by making sure the original data and counterfactuals have similar embeddings. The goal is to balance fairness and utility. Some testing was done on six real-world datasets, and Fairwos performed better than existing methods. |
Keywords
» Artificial intelligence » Gnn
