Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models

no code implementations • CVPR 2022 • Zhibo Wang, Xiaowei Dong, Henry Xue, Zhifei Zhang, Weifeng Chiu, Tao Wei, Kui Ren

Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e. g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice.

Fairness

Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training

no code implementations • 5 Jun 2022 • Guodong Cao, Zhibo Wang, Xiaowei Dong, Zhifei Zhang, Hengchang Guo, Zhan Qin, Kui Ren

However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.

Knowledge Distillation

Towards Fairness-aware Adversarial Network Pruning

no code implementations • ICCV 2023 • Lei Zhang, Zhibo Wang, Xiaowei Dong, Yunhe Feng, Xiaoyi Pang, Zhifei Zhang, Kui Ren

Network pruning aims to compress models while minimizing loss in accuracy.

Fairness Network Pruning