Title: Bias Mitigation Framework for Intersectional Subgroups in Neural Networks. Abstract: We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation framework that prevents models from learning relationships between protected attributes and output variable by reducing mutual information. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that our approach mitigates intersectional bias even when other attributes in the dataset are correlated with protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.
