Large Language Models (LLMs) achieve strong performance in many applications but remain limited in handling graph-structured data due to their reliance on textual context. Recent approaches integrate Graph Neural Networks (GNNs) to enhance structural modeling, yet they largely overlook fairness, leaving models vulnerab...
Zhi-Peng Yin, Zi-Chong Wang, Zhong Chen et al.· Proceedings of the Thirty-Fi...· 5 citations
Graph Neural Networks (GNNs) have demonstrated strong predictive performance across a wide range of applications. However, their increasing deployment has raised critical fairness concerns, as these models can inherit and amplify existing biases. Most existing fairness approaches rely on explicit demographic informatio...
Zi-Chong Wang, Zhi-Peng Yin, Mo Sha et al.· Proceedings of the Thirty-Fi...· 4 citations
The application of question-answering (QA) systems in the medical domain has rapidly advanced, significantly improving patients’ access to reliable health-related information. However, current approaches face notable challenges, including the difficulty in obtaining large-scale and unbiased medical datasets, significan...
Zhi-Peng Yin, Zi-Chong Wang, Min Chen et al.· PLOS Digital Health· 0 citations
Graph Counterfactual Fairness (GCFair), a novel framework that achieves counterfactual fairness by explicitly identifying and disentangling the subsets of node features and graph structures genuinely affected by sensitive attributes, is proposed.
Zi-Chong Wang, Zhi-Peng Yin, Zhong Chen et al.· Proceedings of the Thirty-Fi...· 3 citations
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