  This article is a companion paper to our earlier work Miroshnikov et al.
(2021) on fairness interpretability, which introduces bias explanations. In the
current work, we propose a bias mitigation methodology based upon the
construction of post-processed models with fairer regressor distributions for
Wasserstein-based fairness metrics. By identifying the list of predictors
contributing the most to the bias, we reduce the dimensionality of the problem
by mitigating the bias originating from those predictors. The post-processing
methodology involves reshaping the predictor distributions by balancing the
positive and negative bias explanations and allows for the regressor bias to
decrease. We design an algorithm that uses Bayesian optimization to construct
the bias-performance efficient frontier over the family of post-processed
models, from which an optimal model is selected. Our novel methodology performs
optimization in low-dimensional spaces and avoids expensive model retraining.
