Disentangled contrastive learning for fair graph representations.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Theory

Background:

• Graph Neural Networks (GNNs) are crucial for learning from graph-structured data.
• GNN predictions can be biased due to sensitive attributes, leading to discrimination.
• There is an urgent need for methods to ensure fairness in GNN applications.

Purpose of the Study:

• To propose a novel framework, the Fair Disentangled Graph Neural Network (FDGNN), for learning fair node representations.
• To address algorithmic discrimination in GNNs and protect vulnerable groups.
• To build more trustworthy artificial intelligence systems.

Main Methods:

• FDGNN enhances data diversity via augmentation, creating instances with identical sensitivity but different graph structures.
• A counterfactual augmentation strategy balances sensitive attribute distributions across groups.
• Disentangled contrastive learning separates sensitive from non-sensitive attributes for fair predictions.

Main Results:

• FDGNN demonstrated superior fairness in predictions across three real-world datasets.
• The framework effectively learned disentangled representations, minimizing sensitive information's impact.
• Experimental results validate the efficacy of FDGNN compared to baseline methods.

Conclusions:

• FDGNN offers a robust solution for achieving fairness in graph neural networks.
• Disentanglement is a promising technique for learning fair representations in graph data.
• The framework contributes to the development of trustworthy and equitable AI.