Researchers in explainable artificial intelligence have developed numerous
methods for helping users understand the predictions of complex supervised
learning models. By contrast, explaining the $\textit{uncertainty}$ of model
outputs has received relatively little attention. We adapt the popular Shapley
value framework to explain various types of predictive uncertainty, quantifying
each feature's contribution to the conditional entropy of individual model
outputs. We consider games with modified characteristic functions and find deep
connections between the resulting Shapley values and fundamental quantities
from information theory and conditional independence testing. We outline
inference procedures for finite sample error rate control with provable
guarantees, and implement an efficient algorithm that performs well in a range
of experiments on real and simulated data. Our method has applications to
covariate shift detection, active learning, feature selection, and active
feature-value acquisition.

本文提出一种将 Shapley 值框架用于解释各种预测不确定性的方法，旨在通过量化每个特征对个体模型输出的条件熵的贡献来解释模型输出的不确定度；该方法具有用于协变量转移检测、主动学习、特征选择和主动特征值获取的应用。