  Despite recent advances in the field of explainable artificial intelligence
systems, a concrete quantitative measure for evaluating the usability of such
systems is nonexistent. Ensuring the success of an explanatory interface in
interacting with users requires a cyclic, symbiotic relationship between human
and artificial intelligence. We, therefore, propose explanatory efficacy, a
novel metric for evaluating the strength of the cyclic relationship the
interface exhibits. Furthermore, in a user study, we evaluated the perceived
affect and workload and recorded the EEG signals of our participants as they
interacted with our custom-built, iterative explanatory interface to build
personalized recommendation systems. We found that systems for perceptually
driven iterative tasks with greater explanatory efficacy are characterized by
statistically significant hemispheric differences in neural signals with 62.4%
accuracy, indicating the feasibility of neural correlates as a measure of
explanatory efficacy. These findings are beneficial for researchers who aim to
study the circular ecosystem of the human-artificial intelligence partnership.
