Towards A Scalable Solution for Improving Multi-Group Fairness in
  Compositional Classification
James Atwood Tina Tian Ben Packer Meghana Deodhar Jilin Chen Alex Beutel Flavien Prost Ahmad Beirami
Despite the rich literature on machine learning fairness, relatively little
attention has been paid to remediating complex systems, where the final
prediction is the combination of multiple classifiers and where multiple groups
are present. In this paper, we first show that natural baseline approaches for
improving equal opportunity fairness scale linearly with the product of the
number of remediated groups and the number of remediated prediction labels,
rendering them impractical. We then introduce two simple techniques, called
{\em task-overconditioning} and {\em group-interleaving}, to achieve a constant
scaling in this multi-group multi-label setup. Our experimental results in
academic and real-world environments demonstrate the effectiveness of our
proposal at mitigation within this environment.