  Societal biases are reflected in large pre-trained language models and their
fine-tuned versions on downstream tasks. Common in-processing bias mitigation
approaches, such as adversarial training and mutual information removal,
introduce additional optimization criteria, and update the model to reach a new
debiased state. However, in practice, end-users and practitioners might prefer
to switch back to the original model, or apply debiasing only on a specific
subset of protected attributes. To enable this, we propose a novel modular bias
mitigation approach, consisting of stand-alone highly sparse debiasing
subnetworks, where each debiasing module can be integrated into the core model
on-demand at inference time. Our approach draws from the concept of \emph{diff}
pruning, and proposes a novel training regime adaptable to various
representation disentanglement optimizations. We conduct experiments on three
classification tasks with gender, race, and age as protected attributes. The
results show that our modular approach, while maintaining task performance,
improves (or at least remains on-par with) the effectiveness of bias mitigation
in comparison with baseline finetuning. Particularly on a two-attribute
dataset, our approach with separately learned debiasing subnetworks shows
effective utilization of either or both the subnetworks for selective bias
mitigation.
