Towards a Systematic Model of the Effects of Transparency Utterances on Calibrating Trust in Social Robots

This paper presents the theoretical basis for understanding how transparency utterances in social robots can be systematically employed to regulate trust. Current approaches to trust calibration often lack more detailed insights into how different verbal explanations of actions or intentions shape users' mental models of the robot's capabilities. Drawing on pragmatic principles and mechanisms from human interaction research, we present a model of how transparency utterances contribute to mental models of the robotic interaction partner. We test the model in a user study on the effects of transparency utterances that allow inferences about higher- or lower-level capabilities. The in-person, between-subject experiment with N=47 shows that depending on the design of the transparency utterance, users perceive the competence, benevolence and transparency of the robot differently and infer more or fewer additional capabilities. The results confirm the effect of the proposed design of transparency utterances on (over-)trust. Our model thus offers a theoretical foundation for developing transparency strategies in various contexts of human-robot interaction.