  The rapid growth of research in explainable artificial intelligence (XAI)
follows on two substantial developments. First, the enormous application
success of modern machine learning methods, especially deep and reinforcement
learning, which have created high expectations for industrial, commercial and
social value. Second, the emergence of concern for creating trusted AI systems,
including the creation of regulatory principles to ensure transparency and
trust of AI systems.These two threads have created a kind of "perfect storm" of
research activity, all eager to create and deliver it any set of tools and
techniques to address the XAI demand. As some surveys of current XAI suggest,
there is yet to appear a principled framework that respects the literature of
explainability in the history of science, and which provides a basis for the
development of a framework for transparent XAI. Here we intend to provide a
strategic inventory of XAI requirements, demonstrate their connection to a
history of XAI ideas, and synthesize those ideas into a simple framework to
calibrate five successive levels of XAI.
