Recent research has identified discriminatory behavior of automated
prediction algorithms towards groups identified on specific protected
attributes (e.g., gender, ethnicity, age group, etc.). When deployed in
real-world scenarios, such techniques may demonstrate biased predictions
resulting in unfair outcomes. Recent literature has witnessed algorithms for
mitigating such biased behavior mostly by adding convex surrogates of fairness
metrics such as demographic parity or equalized odds in the loss function,
which are often not easy to estimate. This research proposes a novel
in-processing based GroupMixNorm layer for mitigating bias from deep learning
models. 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 overall accuracy. Analysis on benchmark tabular and image datasets
demonstrates the efficacy of the proposed method in achieving state-of-the-art
performance. Further, the experimental analysis also suggests the robustness of
the GroupMixNorm layer against new protected attributes during inference and
its utility in eliminating bias from a pre-trained network.

提出了一种基于 GroupMixNorm 层的处理方法，通过混合样本的组级特征统计数据来减轻深度学习模型中的偏见，该方法在减少偏见的同时对整体准确性影响较小。实验结果表明该方法在标准数据集上取得了最先进的性能，同时还表明 GroupMixNorm 层对于新的受保护属性在推理过程中的稳健性及其在预训练网络中消除偏见的效用。