Protocol-Conditional Attribution in Fairness-Aware Graph Neural Networks

Fairness-aware graph neural networks (GNNs) are typically evaluated by comparing a complete method with an external baseline, even when the scientific claim concerns an intervention intended to reduce bias. Such comparisons show what the complete method does, but not how much of the observed change is associated with the claimed intervention. We separate the complete-method contrast from a matched intervention contrast between intervention-disabled and intervention-enabled versions of the same method, and treat this intervention attribution as conditional on the protocol used to train, select, and evaluate the model, an approach we call protocol-conditional attribution. Across 36 controlled cells spanning eleven methods, the surrounding package has a larger absolute contrast than the claimed intervention in 26/36 cells for demographic parity and 29/36 for equal opportunity (about one half against a more strongly trained baseline), and the intervention contrast is resolved in only 8/36 and 9/36 cells, with most remaining fairness contrasts inconclusive rather than negligible. Changes in method configuration and checkpoint selection frequently alter the magnitude and resolution of intervention attribution, although resolved directions are rarely reversed. Targeted analyses show that longer training can change the intervention contrast at later checkpoints and that an explicit fairness correction can contribute little relative to preceding components. Claims about why a system is fairer therefore require matched intervention evidence under an explicit protocol. Our code is available at https://anonymous.4open.science/r/FairGNN-Eval-ICLR.