Perturbation Effects on Accuracy and Fairness among Similar Individuals

ArXi:2404.01356v3 Announce Type: replace-cross Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings. However, existing evaluation protocols typically assess these dimensions in isolation, thereby obscuring critical failure modes.