  We present a scalable post-processing algorithm for debiasing trained models,
including deep neural networks (DNNs), which we prove to be near-optimal by
bounding its excess Bayes risk. We empirically validate its advantages on
standard benchmark datasets across both classical algorithms as well as modern
DNN architectures and demonstrate that it outperforms previous post-processing
methods while performing on par with in-processing. In addition, we show that
the proposed algorithm is particularly effective for models trained at scale
where post-processing is a natural and practical choice.
