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Found 3 papers, 1 papers with code
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectrum.
FairGT: A Fairness-aware Graph Transformer
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
Algorithmic Fairness: A Tolerance Perspective
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups.
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