Classification Rules Explain Machine Learning

We introduce a general model for explainable Artificial Intelligence that identifies an explanation of a Machine Learning method by classification rules. We define a notion of distance between two Machine Learning methods, and provide a method that computes a set of classification rules that, in turn, approximates another black box method to a given extent. We further build upon this method an anytime algorithm that returns the best approximation it can compute within a given interval of time. This anytime method returns the minimum and maximum difference in terms of approximation provided by the algorithm and uses it to determine whether the obtained approximation is acceptable. We then illustrate the results of a few experiments on three different datasets that show certain properties of the approximations that should be considered while modelling such systems. On top of this, we design a methodology for constructing approximations for ML, that we compare to the no-methods approach typically used in current studies on the explainable artificial intelligence topic.