Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test

This paper introduces the "grasp-ability test" as a "goodness" criteria by which to compare which explanation is more or less meaningful than others for users to understand the automated algorithmic data processing. A growing number of researchers attempt to develop explainable AIs (hereafter, XAI) to meet practical (e.g., explainability is positively correlated to users' learning performance), legal (e.g., explainability is required for S.E.C. to scrutinize AIpowered trading techniques; liability issues) and ethical expectations (e.g., right to explanation; trust; autonomy). Different researchers use different ideas of what an explanation is [1]. For example, as Figure 1 shows, 11 U.S. research groups, funded by DARPA, are currently developing XAI in different manners. Then, a question is raised: how can we know which model of XAI is good enough or better/worse than others? To answer this, we need a "goodness" criteria.