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Explainable AI Beer Style Classifier

This paper describes how to build an eXplainable Artificial Intelligent (XAI) classifier for a real use case related to beer style classification. It combines an opaque machine learning algorithm (Random Forest) with an interpretable machine learning algorithm (Decision Tree). The result is a XAI classifier which provides users with a good interpretability-accuracy trade-off but also with explanation capabilities. First, the opaque algorithm acts as an “oracle” which finds out the most plausible output. Then, we generate a textual explanation of the given output which emerges as an automatic interpretation of the inference process carried out by the related decision tree, if the outputs from both classifiers coincide. We apply a Natural Language Generation Approach to generate the textual explanations.