  We characterize three notions of explainable AI that cut across research
fields: opaque systems that offer no insight into its algo- rithmic mechanisms;
interpretable systems where users can mathemat- ically analyze its algorithmic
mechanisms; and comprehensible systems that emit symbols enabling user-driven
explanations of how a conclusion is reached. The paper is motivated by a corpus
analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences
in how work on explainable AI is positioned in various fields. We close by
introducing a fourth notion: truly explainable systems, where automated
reasoning is central to output crafted explanations without requiring human
post processing as final step of the generative process.
