The most widely studied explainable AI (XAI) approaches are unsound. This is the case with
well-known model-agnostic explanation approaches, and it is also the case with approaches
based on saliency maps. One solution is to consider intrinsic interpretability, which does not
exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display
unwieldy explanation redundancy. Formal explainability represents the alternative to these
non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI …