Better Data from AI Users: A Field Experiment on the Impacts of Robot Self-Disclosure on the Utterance of Child Users in Home Environment

Abstract

Data are one of the important factors in artificial intelligence (AI). Moreover, in order for AI to understand the user and go beyond the role of a simple machine, the data contained in the user’s self-disclosure is required. In this study, two types of robot self-disclosures (disclosing robot utterance, involving user utterance) are proposed to elicit higher self-disclosure from AI users. Additionally, this study examines the moderating effects of multi-robot conditions. In order to investigate these effects empirically and increase the implications of research, a field experiment with prototypes was conducted in the context of using smart speaker of children. The results indicate that both types of robot self-disclosures were effective in eliciting the self-disclosure of children. The interaction effect between disclosing robot and involving user was found to take a different direction depending on the sub-dimension of the user’s self-disclosure. Multi-robot conditions partially moderate the effects of the two types of robot self-disclosures.