This paper proposes an alternative approach to the basic taxonomy of explanations produced by explainable artificial intelligence techniques. Methods of Explainable Artificial Intelligence (XAI) were developed to answer the question why a certain prediction or estimation was made, preferably in terms easy to understand by the human agent. XAI taxonomies proposed in the literature mainly concentrate their attention on distinguishing explanations with respect to involving the human agent, which makes it complicated to provide a more mathematical approach to distinguish and compare different explanations. This paper narrows its attention to the cases where the data set of interest belongs to $\mathbb{R} ^n$ and proposes a simple linear algebra-based taxonomy for local explanations.

翻译：本文提出了一种替代性方法来对可解释人工智能技术生成的解释进行基础分类。可解释人工智能（XAI）方法旨在回答为何做出特定预测或估计的问题，并优先采用人类易于理解的术语进行表达。现有文献提出的XAI分类法主要关注区分涉及人类代理的解释，这导致难以提供更数学化的方法来区分和比较不同解释。本文将关注范围缩小至数据集属于$\mathbb{R}^n$的情形，并提出了一种基于简单线性代数的局部解释分类法。