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RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search by Li, Xuran; Wu, Peng; Dong, Kaixiang; Zhang, Zhen; Chen, Yanting is a scholarly article available to read on EtoBox.

What is RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search about?

Deep neural networks (DNNs) often face challenges due to their vulnerability to various adversarial perturbations, including false perturbations that undermine prediction accuracy and biased perturbations that cause biased predictions for similar inputs. This paper introduces a novel approach, RobustFair, to evaluate the accurate fairness of DNNs when subjected to these false or biased perturbations. RobustFair employs the notion of the fairness confusion matrix induced in accurate fairness to identify the crucial input features for perturbations. This matrix categorizes predictions as true fair, true biased, false fair, and false biased, and the perturbations guided by it can produce a dual impact on instances and their similar counterparts to either undermine prediction accuracy (robustness) or cause biased predictions (individual fairness). RobustFair then infers the ground truth of these generated adversarial instances based on their loss function values approximated by the total derivative. To leverage the generated instances for trustworthiness improvement, RobustFair further proposes a data augmentation strategy to prioritize adversarial instances resembling the original tra

Author

Li, Xuran; Wu, Peng; Dong, Kaixiang; Zhang, Zhen; Chen, Yanting

Published

2023

Language

EN