  The objective of this article is to introduce a fairness interpretability
framework for measuring and explaining the bias in classification and
regression models at the level of a distribution. In our work, we measure the
model bias across sub-population distributions in the model output using the
Wasserstein metric. To properly quantify the contributions of predictors, we
take into account the favorability of both the model and predictors with
respect to the non-protected class. The quantification is accomplished by the
use of transport theory, which gives rise to the decomposition of the model
bias and bias explanations to positive and negative contributions. To gain more
insight into the role of favorability and allow for additivity of bias
explanations, we adapt techniques from cooperative game theory.
