Fairness Increases Adversarial Vulnerability
This addresses a critical trade-off between fairness and security in AI systems, particularly for applications like facial recognition, but is incremental as it builds on known dichotomies.
The paper demonstrates that achieving fairness in deep learning models can increase their vulnerability to adversarial attacks, identifying distance to the decision boundary as a key factor, and proposes a method to balance fairness and robustness with validated experiments across vision domains.
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 the model robustness to adversarial samples. The reported analysis sheds light on the factors causing such contrasting behavior, suggesting that distance to the decision boundary across groups as a key explainer for this behavior. Extensive experiments on non-linear models and different architectures validate the theoretical findings in multiple vision domains. Finally, the paper proposes a simple, yet effective, solution to construct models achieving good tradeoffs between fairness and robustness.