GroupMixNorm Layer for Learning Fair Models
Anubha Pandey Aditi Rai Maneet Singh Deepak Bhatt Tanmoy Bhowmik
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.