TY - JOUR
T1 - Justified True Belief and Robot Knowledge: User Perceptions in Human–Robot Interaction
AU - Peter Nilsson
AU - Eva Johansson
AU - Lars Andersson
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2023
VL - 2
IS - 1
SP - 24
N2 - This research examined user perceptions regarding the concept of "knowledge" in robotic systems and how this conceptualization diverges from perspectives on human knowledge. Utilizing the Justified True Belief (JTB) framework of epistemology, three experiments were executed to ascertain whether individuals apply identical criteria to a robot's epistemic states as they do to a human's. In Experiment 1, a Gettier-style scenario was presented to participants; their subsequent responses demonstrated that the prerequisites for attributing knowledge vary significantly between human and robotic agents. Experiment 2 investigated the precise threshold at which information is categorized as "knowledge," indicating that users demand a higher degree of direct verification from robots than from humans. Experiment 3 analyzed how these underlying beliefs regarding robotic knowledge, as mapped in Experiments 1 and 2, affect the moral assessments made by users. The findings indicated that when acting as an accomplice to an offense, a robot was assigned less responsibility by users than a human accomplice. Collectively, these outcomes imply that users view a robot's knowledge as rigid, dependent upon direct perception, and inherently distinct from human cognition and inference. These insights offer valuable implications for designing human–robot interaction (HRI) systems, as comprehending user perceptions of robotic knowledge may be critical for fostering trust in future human–robot collaboration.
UR - https://tipublications.com/n179081048
ER -