Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity. To address this problem, we propose a novel method SHEAR to significantly accelerate the Shapley explanation for DNN models, where only a few coalitions of input features are involved in the computation. The selection of the feature coalitions follows our proposed Shapley chain rule to minimize the absolute error from the ground-truth Shapley values, such that the computation can be both efficient and accurate. To demonstrate the effectiveness, we comprehensively evaluate SHEAR across multiple metrics including the absolute error from the ground-truth Shapley value, the faithfulness of the explanations, and running speed. The experimental results indicate SHEAR consistently outperforms state-of-the-art baseline methods across different evaluation metrics, which demonstrates its potentials in real-world applications where the computational resource is limited.

翻译：尽管Shapley值为深度神经网络（DNN）模型预测提供了有效的解释，但其计算依赖于枚举所有可能的输入特征联盟，导致复杂度呈指数级增长。为解决这一问题，我们提出了一种新颖方法SHEAR，显著加速了DNN模型的Shapley解释，该方法仅需计算少量输入特征联盟。特征联盟的选择遵循我们提出的Shapley链式法则，以最小化与真实Shapley值的绝对误差，从而兼顾计算效率与准确性。为验证有效性，我们基于多个指标对SHEAR进行了全面评估，包括与真实Shapley值的绝对误差、解释的忠实度以及运行速度。实验结果表明，SHEAR在不同评估指标上均持续优于现有最先进的基线方法，展示了其在计算资源受限的实际应用中的潜力。