"Who Should I Believe?": User Interpretation and Decision-Making When a Family Healthcare Robot Contradicts Human Memory

TL;DR

This study investigates how transparency and sociability of healthcare robots influence user trust, interpretation, and decision-making when faced with conflicting information, revealing tendencies toward overtrust and attribution biases.

Contribution

It provides empirical evidence on the effects of robot transparency and sociability on user perceptions and trust in healthcare robots in home settings.

Findings

Higher transparency leads users to attribute discrepancies to external factors.

Users tend to overtrust robot recommendations despite suspicions of malfunction.

Transparency influences how users interpret conflicting robot information.

Abstract

Advancements in robotic capabilities for providing physical assistance, psychological support, and daily health management are making the deployment of intelligent healthcare robots in home environments increasingly feasible in the near future. However, challenges arise when the information provided by these robots contradicts users' memory, raising concerns about user trust and decision-making. This paper presents a study that examines how varying a robot's level of transparency and sociability influences user interpretation, decision-making and perceived trust when faced with conflicting information from a robot. In a 2 x 2 between-subjects online study, 176 participants watched videos of a Furhat robot acting as a family healthcare assistant and suggesting a fictional user to take medication at a different time from that remembered by the user. Results indicate that robot transparency influenced users' interpretation of information discrepancies: with a low transparency robot, the most frequent assumption was that the user had not correctly remembered the time, while with the high transparency robot, participants were more likely to attribute the discrepancy to external factors, such as a partner or another household member modifying the robot's information. Additionally, participants exhibited a tendency toward overtrust, often prioritizing the robot's recommendations over the user's memory, even when suspecting system malfunctions or third-party interference. These findings highlight the impact of transparency mechanisms in robotic systems, the complexity and importance associated with system access control for multi-user robots deployed in home environments, and the potential risks of users' over reliance on robots in sensitive domains such as healthcare.