Looking across the scholastic and scientific literatures on explanation in such disciplines as psychology, philosophy, and instructional design, we find assertions about what makes for a good explanation. There is a general consensus on factors such as clarity and precision. Thus, one can look at a given explanation and make an a priori (or decontextualized) judgment as to whether or not it is “good.”
Good explanations are said to be ones that are plausible and internally consistent, have an appropriate amount of detail and a clear focus, are veridical or accurate with respect to the thing being explained, are useful for the intended user, are clear and understandable.
The Goodness Checklist is for researchers or domain experts who want to conduct an independent, a priori evaluation of the goodness of explanations that are generated by XAI systems. Using the Goodness Checklist, independent judges ask, Are the researchers right in claiming that their explanations are good?
Although scale validity has been evaluated, data on the scale reliability would be of value.