Mitigating Robustness Bias: Theoretical Results and Empirical Evidences

Recent research has shown that some learned classifiers can be more easily fooled by an adversary who carefully crafts imperceptible or physically plausible modifications of the input data regarding particular subgroups of the population (e.g., people with particular gender, ethnicity, or skin color). This form of unfairness has been just recently studied, noting the fact that classical fairness metrics, which only observe the model outputs, are not enough but robustness biases need to be measured and mitigated as well. For this reason, in this paper, we will first develop a new metric of fairness which generalizes the current ones and degenerates in the classical ones and then we will develop a theoretical mitigation framework with consistency results able to generate a new empirical mitigation strategy and explain why the current ones actually work.