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

A multi-component framework for the analysis and design of explainable artificial intelligence