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.

本文介绍了一种公平解释性框架，用于在分布级别上测量和解释分类和回归模型中的偏见。通过使用 Wasserstein 度量，在模型输出的亚群分布上测量模型偏差，考虑模型和预测变量相对于非受保护类的有利性，使用运输理论进行量化，并通过博弈理论的技术实现偏见解释的可加性。