  Interpretability and explainability are among the most important challenges
of modern artificial intelligence, being mentioned even in various legislative
sources. In this article, we develop a method for extracting immediately human
interpretable classifiers from tabular data. The classifiers are given in the
form of short Boolean formulas built with propositions that can either be
directly extracted from categorical attributes or dynamically computed from
numeric ones. Our method is implemented using Answer Set Programming. We
investigate seven datasets and compare our results to ones obtainable by
state-of-the-art classifiers for tabular data, namely, XGBoost and random
forests. Over all datasets, the accuracies obtainable by our method are similar
to the reference methods. The advantage of our classifiers in all cases is that
they are very short and immediately human intelligible as opposed to the
black-box nature of the reference methods.
