Mining User Study Data to Judge the Merit of a Model for Supporting User-Specific Explanations of AI Systems

In this paper, we present a model for supporting user-specific explanations of AI systems. We then discuss a user study that was conducted to gauge whether the decisions for adjusting output to users with certain characteristics was confirmed to be of value to participants. We focus on the merit of having explanations attuned to particular psychological profiles of users, and the value of having different options for the level of explanation that is offered (including allowing for no explanation, as one possibility). Following the description of the study, we present an approach for mining data from user participant responses in order to determine whether the model that was developed for varying the output to users was well-founded. While our results in this respect are preliminary, we explain how using varied machine learning methods is of value as a concrete step toward validation of specific approaches for AI explanation. We conclude with a discussion of related work and some ideas for new directions with the research, in the future.