Towards Fairness-aware Adversarial Network Pruning

Abstract

Network pruning aims to compress models while minimizing loss in accuracy. With the increasing focus on bias in AI systems, the bias inheriting or even magnification nature of traditional network pruning methods has raised a new perspective towards fairness-aware network pruning. Straightforward pruning plus debias methods and recent designs for monitoring disparities of demographic attributes during pruning have endeavored to enhance fairness in pruning. However, neither simple assembling of two tasks nor specifically designed pruning strategies could achieve the optimal trade-off among pruning ratio, accuracy, and fairness. This paper proposes an end-to-end learnable framework for fairness-aware network pruning, which optimizes both pruning and debias tasks jointly by adversarial training against those final evaluation metrics like accuracy for pruning, and disparate impact (DI) and equalized odds (DEO) for fairness. In other words, our fairness-aware adversarial pruning method would learn to prune without any handcraft rules. Therefore, our approach could flexibly adapt to variate network structures. Exhaustive experimentation demonstrates the generalization capacity of our approach, as well as superior performance on pruning and debias simultaneously. To highlight, the proposed method could preserve the SOTA pruning performance while significantly improving fairness by around 50% as compared to traditional pruning methods.