Recover Fair Deep Classification Models via Altering Pre-trained Structure
Yanfu Zhang, Shangqian Gao, Heng Huang
"There have been growing interest in algorithmic fairness for biased data. Although various pre-, in-, and post-processing methods are designed to address this problem, new learning paradigms designed for fair deep models are still necessary. Modern computer vision tasks usually involve large generic models and fine-tuning concerning a specific task. Training modern deep models from scratch is expensive considering the enormous training data and the complicated structures. The recently emerged intra-processing methods are designed to debias pre-trained large models. However, existing techniques stress fine-tuning more, but the deep network structure is less leveraged. This paper proposes a novel intra-processing method to improve model fairness by altering the deep network structure. We find that the unfairness of deep models are usually caused by a small portion of sub-modules, which can be uncovered using the proposed differential framework. We can further employ several strategies to modify the corrupted sub-modules inside the unfair pre-trained structure to build a fair counterpart. We experimentally verify our findings and demonstrate that the reconstructed fair models can make fair classification and achieve superior results to the state-of-the-art baselines. We conduct extensive experiments to evaluate the different strategies. The results also show that our method has good scalability when applied to a variety of fairness measures and different data types."