SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on prediction machine learning model. Despite lot recent interest from both academia and industry, it is not known whether common models can be computed efficiently. In this paper, we establish complexity computing explanation i...