  Despite significant research efforts, deep neural networks are still
vulnerable to biases: this raises concerns about their fairness and limits
their generalization. In this paper, we propose a bias-agnostic approach to
mitigate the impact of bias in deep neural networks. Unlike traditional
debiasing approaches, we rely on a metric to quantify ``bias
alignment/misalignment'' on target classes, and use this information to
discourage the propagation of bias-target alignment information through the
network. We conduct experiments on several commonly used datasets for debiasing
and compare our method to supervised and bias-specific approaches. Our results
indicate that the proposed method achieves comparable performance to
state-of-the-art supervised approaches, although it is bias-agnostic, even in
presence of multiple biases in the same sample.
