This paper develops a rigorous argument for why the use of Shapley values in explainable AI (XAI) will necessarily yield provably misleading information about the relative importance of features for predictions. Concretely, this paper demonstrates that there exist classifiers, and associated predictions, for which the relative importance of features determined by the Shapley values will incorrectly assign more importance to features that are provably irrelevant for the prediction, and less importance to features that are provably relevant for the prediction. The paper also argues that, given recent complexity results, the existence of efficient algorithms for the computation of rigorous feature attribution values in the case of some restricted classes of classifiers should be deemed unlikely at best.
翻译:本文就为何在可解释的AI(XAI)中使用Shapley 值必然会产生关于预测特征相对重要性的可辨别的误导性信息,提出了严格的论据。具体地说,本文件表明,存在分类师和相关预测,而Shapley 值所确定特征的相对重要性将错误地赋予与预测无关的特征以更大的重要性,而对于与预测有关的特征则不那么重要。本文件还认为,鉴于最近的复杂结果,在一些受限制分类师类别中,为计算严格的特征归属值而存在有效的算法,充其量是不可能的。