Abstract Discussions surrounding questions of interpretability (or even: explainability) in AI and ML are gaining in popularity in academia and industry. I will briefly characterize four notions of explainable AI/ML that cut across research fields: 1) opaque systems that offer no insight whatsoever, 2) interpretable systems where users can mathematically analyse the mechanisms at work, 3) comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached,  and 4) explainable systems, where automated reasoning is central to…