Explaining Predictive Uncertainty with Information Theoretic Shapley Values
This addresses the need for interpretability in model uncertainty for users in fields like machine learning and data science, representing a novel method for a known bottleneck.
The paper tackles the problem of explaining predictive uncertainty in supervised learning models by adapting the Shapley value framework to quantify feature contributions to conditional entropy, with applications in covariate shift detection and active learning.
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 efficient algorithms that perform 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.