Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?

Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang

Keywords: explainable AI, attribution methods, deep neural networks

Abstract: Masking some input variables of a deep neural network (DNN) and computing output changes on the masked input sample represent a typical way to compute attributions of input variables in the sample. People usually mask an input variable using its baseline value. However, there is no theory to examine whether baseline value faithfully represents the absence of an input variable, i.e., removing all signals from the input variable. Fortunately, recent studies (Ren et al., 2023a; Deng et al., 2022a) show that the inference score of a DNN can be strictly disentangled into a set of causal patterns (or concepts) encoded by the DNN. Therefore, we propose to use causal patterns to examine the faithfulness of baseline values. More crucially, it is proven that causal patterns can be explained as the elementary rationale of the Shapley value. Furthermore, we propose a method to learn optimal baseline values, and experimental results have demonstrated its effectiveness.

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TL;DR: We propose a method to examine and learn baseline values for Shapley values, which ensures that the absent variables do not introduce information to the model.