Interpreting Robot Failure and Deception: The Role of Human-Likeness

Abstract

How people interpret robot errors may depend not only on the observed behavior, but also on the robot's morphology and on whether the error is framed as unintentional or intentional. Prior works showed that human-like robots can elicit stronger mind attribution and deceptive robot behavior can encourage intentional interpretations, but their joint effect on the same erroneous action remains unclear. We addressed this question in an online video-based study in which participants $(N=71)$ watched four robots differing in human-likeness (within-subject factor: Temi, Pepper, Miroka, and Ameca) produce the same incorrect response in an escapegame scenario. The error was framed either as a failure or as an intentional error, namely deception sustained by simulated social motives. Quantitative results showed that Ameca received higher mind attribution ratings than the other robots, while deceptive framing increased mind attribution across all robot morphologies. No interaction emerged between morphology and framing. Thematic analysis showed that unintentional errors were mainly interpreted in terms of compliance, programming, and system limitations, whereas deceptively framed errors elicited more social and relational explanations.