  Existing pruning techniques preserve deep neural networks' overall ability to
make correct predictions but may also amplify hidden biases during the
compression process. We propose a novel pruning method, Fairness-aware GRAdient
Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of
pruning on different sub-groups. Our method calculates the per-group importance
of each model weight and selects a subset of weights that maintain the relative
between-group total importance in pruning. The proposed method then prunes
network edges with small importance values and repeats the procedure by
updating importance values. We demonstrate the effectiveness of our method on
four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks
of face attribute classification where our method reduces the disparity in
performance degradation by up to 90% compared to the state-of-the-art pruning
algorithms. Our method is substantially more effective in a setting with a high
pruning rate (99%). The code and dataset used in the experiments are available
at https://github.com/Bernardo1998/FairGRAPE
