The Effects of Expertise, Humanness, and Congruence on Perceived Trust, Warmth, Competence and Intention to Use Embodied AI
Philipp Krop, Martin J. Koch, Astrid Carolus, Marc Erich Latoschik, Carolin Wienrich
Abstract
Even though people imagine different embodiments when asked which AI they would like to work with, most studies investigate trust in AI systems without specific physical appearances. This study aims to close this gap by combining influencing factors of trust to analyze their impact on the perceived trustworthiness, warmth, and competence of an embodied AI. We recruited 68 participants who observed three co-working scenes with an embodied AI, presented as expert/novice (expertise), human/AI (humanness), or congruent/slightly incongruent to the environment (congruence). Our results show that the expertise condition had the largest impact on trust, acceptance, and perceived warmth and competence. When controlled for perceived competence, the humanness of the AI and the congruence of its embodiment to the environment also influence acceptance. The results show that besides expertise and the perceived competence of the AI, other design variables are relevant for successful human-AI interaction, especially when the AI is embodied.