Fairness Increases Adversarial Vulnerability. (arXiv:2211.11835v2 [cs.LG] UPDATED)

The remarkable performance of deep learning models and their applications in
consequential domains (e.g., facial recognition) introduces important
challenges at the intersection of equity and security. Fairness and robustness
are two desired notions often required in learning models. Fairness ensures
that models do not disproportionately harm (or benefit) some groups over
others, while robustness measures the models' resilience against small input
perturbations.

This paper shows the existence of a dichotomy between fairness and
robustness, and analyzes when achieving fairness decreases …

adversarial fairness vulnerability