  Explainable AI is an emerging field providing solutions for acquiring
insights into automated systems' rationale. It has been put on the AI map by
suggesting ways to tackle key ethical and societal issues. Existing explanation
techniques are often not comprehensible to the end user. Lack of evaluation and
selection criteria also makes it difficult for the end user to choose the most
suitable technique. In this study, we combine logic-based argumentation with
Interpretable Machine Learning, introducing a preliminary meta-explanation
methodology that identifies the truthful parts of feature importance oriented
interpretations. This approach, in addition to being used as a meta-explanation
technique, can be used as an evaluation or selection tool for multiple feature
importance techniques. Experimentation strongly indicates that an ensemble of
multiple interpretation techniques yields considerably more truthful
explanations.
