  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.
