The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness

arXiv:2404.01356v1 Announce Type: new
Abstract: Deep neural networks (DNNs) are known to be sensitive to adversarial input perturbations, leading to a reduction in either prediction accuracy or individual fairness. To jointly characterize the susceptibility of prediction accuracy and individual fairness to adversarial perturbations, we introduce a novel robustness definition termed robust accurate fairness. Informally, robust accurate fairness requires that predictions for an instance and its similar counterparts consistently align with the ground truth when subjected to input perturbations. We propose …

abstract accuracy adversarial arxiv cs.ai cs.cy cs.lg definition fairness networks neural networks novel prediction robust robustness type