  Model pruning can enable the deployment of neural networks in environments
with resource constraints. While pruning may have a small effect on the overall
performance of the model, it can exacerbate existing biases into the model such
that subsets of samples see significantly degraded performance. In this paper,
we introduce the performance weighted loss function, a simple modified
cross-entropy loss function that can be used to limit the introduction of
biases during pruning. Experiments using biased classifiers for facial
classification and skin-lesion classification tasks demonstrate that the
proposed method is a simple and effective tool that can enable existing pruning
methods to be used in fairness sensitive contexts.
