AINeutralarXiv – CS AI · Apr 156/10
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GF-Score: Certified Class-Conditional Robustness Evaluation with Fairness Guarantees
Researchers introduce GF-Score, a framework that evaluates neural network robustness across individual classes while measuring fairness disparities, eliminating the need for expensive adversarial attacks through self-calibration. Testing across 22 models reveals consistent vulnerability patterns and shows that more robust models paradoxically exhibit greater class-level fairness disparities.