  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 approach that prevents models from learning
relationships between protected attributes and output variable by reducing
mutual information between them. We demonstrate that our approach is effective
in reducing bias with little or no drop in accuracy. We also show that the
models trained with our learning framework become causally fair and insensitive
to the values of 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.
