GroupMixNorm Layer for Learning Fair Models

Recent research has focused on proposing algorithms for bias mitigation from automated prediction algorithms. Most of the techniques include convex surrogates of fairness metrics such as demographic parity or equalized odds in the loss function, which are not easy to estimate. Further, these fairness constraints are mostly data-dependent and aim to minimize the disparity among the protected groups during the training. However, they may not achieve similar performance on the test set. In order to address the above limitations, this research proposes a novel GroupMixNorm layer for bias mitigation from deep learning models. As an alternative to solving constraint optimization separately for each fairness metric, we have formulated bias mitigation as a problem of distribution alignment of several groups identified through the protected attributes. To this effect, the GroupMixNorm layer probabilistically mixes group-level feature statistics of samples across different groups based on the protected attribute. The proposed method improves upon several fairness metrics with minimal impact on accuracy. Experimental evaluation and extensive analysis on benchmark tabular and image datasets demonstrate the efficacy of the proposed method to achieve state-of-the-art performance.