User-specific explanations of AI systems attuned to psychological profiles: a user study

Abstract

In this thesis, we design a model aimed at supporting user-specific explanations from AI systems and present the results of a user study conducted to determine whether the algorithms used to attune the output to the user match well with the user's own preferences. This is achieved through a dedicated study of certain elements of a user model: levels of neuroticism and extroversion and degree of anxiety towards AI. Our work provides insights into how to test AI theories of explainability with real users, including questionnaires to administer and hypotheses to pose. We also shed some light on the value of a model for generating explanations that reasons about different degrees of and modes of explanation. We conclude with commentary about the continued merit of integrating user modeling into the development of AI explanation solutions, and the challenges, with next steps, to balance the design of theoretical models with the use of empirical evaluation, within the research conducted in the field.