As a favorable tool for explainable artificial intelligence (XAI), Shapley
value has been widely used to interpret deep learning based predictive models.
However, accurate and efficient estimation of Shapley value is a difficult task
since the computation load grows exponentially with the increase of input
features. Most existing accelerated Shapley value estimation methods have to
compromise on estimation accuracy with efficiency. In this article, we present
EmSHAP(Energy model-based Shapley value estimation), which can effectively
approximate the expectation of Shapley contribution function/deep learning
model under arbitrary subset of features given the rest. In order to determine
the proposal conditional distribution in the energy model, a gated recurrent
unit(GRU) is introduced by mapping the input features onto a hidden space, so
that the impact of input feature orderings can be eliminated. In addition, a
dynamic masking scheme is proposed to improve the generalization ability. It is
proved in Theorems 1, 2 and 3 that EmSHAP achieves tighter error bound than
state-of-the-art methods like KernelSHAP and VAEAC, leading to higher
estimation accuracy. Finally, case studies on a medical application and an
industrial application show that the proposed Shapley value-based explainable
framework exhibits enhanced estimation accuracy without compromise on
efficiency.

本文介绍了 EmSHAP（基于能量模型的 Shapley 值估计），它可以有效地近似预测模型在任意特征子集上的 Shapley 贡献函数的期望值。通过引入门控循环单元（GRU）将输入特征映射到隐藏空间，以消除输入特征排序的影响，并提出了动态屏蔽方案来提高泛化能力。定理 1、2 和 3 证明了 EmSHAP 比 KernelSHAP 和 VAEAC 等现有方法具有更紧的误差界限，从而实现了更高的估计精度。最后，针对医学和工业领域的应用案例表明，所提出的基于 Shapley 值的可解释框架具有提高的估计精度而无需牺牲效率。