Congress 1173
  • Jose M. Alonso, A. Ramos-Soto, C. Castiello, C. Mencar
  • The SICSA Reasoning, Learning and Explainability Workshop 2018. Aberdeen, Scotland. 2018

Explainable AI Beer Style Classifier

This paper describes how to build an eXplainable Artificial Intelligence (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. We apply a Natural Language Generation Approach to generate the textual explanations.
Keywords: Explainable Artificial Intelligence, Classification Task, Natural Language Generation
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