Shapley value attribution is an increasingly popular explainable AI (XAI)
method, which quantifies the contribution of each feature to the model's
output. However, recent work has shown that most existing methods to implement
Shapley value attributions have some drawbacks. Due to these drawbacks, the
resulting Shapley value attributions may provide biased or unreliable
explanations, which fail to correctly capture the true intrinsic relationships
between features and model outputs. Moreover, it is difficult to evaluate these
explanation errors because the true underlying dependencies between features
and model outputs are typically unknown. In this paper, we theoretically
analyze the explanation errors of Shapley value attributions by decomposing the
explanation error into two components: observation bias and structural bias. We
also clarify the underlying causes of these two biases and demonstrate that
there is a trade-off between them. Based on this error analysis framework, we
develop two novel concepts: over-informative and under-informative
explanations. We theoretically analyze the potential over-informativeness and
under-informativeness of existing Shapley value attribution methods.
Particularly for the widely deployed assumption-based Shapley value
attributions, we affirm that they can easily be under-informative due to the
distribution drift caused by distributional assumptions. We also propose a
measurement tool to quantify the distribution drift that causes such errors.

本文分析了 Shapley 值归因的解释误差，将解释误差分解为观察偏差和结构偏差两个组成部分，并且证明它们之间存在权衡关系。基于此误差分析框架，提出了过多信息和过少信息解释这两个新概念，并对现有的 Shapley 值归因方法进行了可能的过多信息和过少信息的理论分析。