Summary of Fairsin: Achieving Fairness in Graph Neural Networks Through Sensitive Information Neutralization, by Cheng Yang et al.
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
by Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi
First submitted to arxiv on: 19 Mar 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 In this paper, the authors propose a new approach to ensuring fairness in graph neural networks (GNNs) by incorporating “Fairness-facilitating Features” (F3) into node features or representations. This is a response to the existing methods that filter out sensitive information from inputs or representations, which can result in a sub-optimal trade-off between predictive performance and fairness. The authors demonstrate three implementation variants of their method, FairSIN, and show that it significantly improves fairness metrics while maintaining high prediction accuracies on five benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are great at modeling graph-structured data, but they can also make biased predictions based on sensitive attributes like race or gender. To fix this, some methods try to filter out sensitive information from inputs or representations. But the authors think that’s not a good idea because it can also get rid of important non-sensitive features. Instead, they suggest adding special “Fairness-facilitating Features” (F3) into node features or representations before message passing. This is called FairSIN and it seems to work well. |
