Summary of Endowing Pre-trained Graph Models with Provable Fairness, by Zhongjian Zhang et al.
Endowing Pre-trained Graph Models with Provable Fairness
by Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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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 Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. However, they inherit biases from human society, resulting in discriminatory behavior in downstream applications. The debiasing process of existing fair methods is generally coupled with parameter optimization of GNNs. To overcome these limitations, a novel adapter-tuning framework called GraphPAR was proposed. GraphPAR freezes the parameters of PGMs and trains a parameter-efficient adapter to flexibly improve the fairness of PGMs in downstream tasks. The framework uses sensitive semantic augmenters on node representations and adapters to prevent the propagation of sensitive attribute semantics from PGMs to task predictions. Experimental evaluations demonstrate that GraphPAR achieves state-of-the-art prediction performance and fairness on node classification tasks, with around 90% nodes having provable fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pre-trained graph models try to learn about structures in data and use this knowledge for different tasks. But they can be unfair because they were trained on biased data. Existing methods to make these models fair require adjusting the model’s parameters. However, each task may have its own sensitive attributes, making it inflexible and inefficient. To fix this, a new method called GraphPAR was developed. It freezes the original model’s parameters and trains an adapter that can improve fairness without affecting the original model. This adapter uses special techniques to prevent unfairness from spreading. The results show that GraphPAR is very good at balancing performance and fairness. |
Keywords
* Artificial intelligence * Classification * Optimization * Parameter efficient * Semantics
