Impact of Design Transparency on Trust and Data Sharing during Human-Robot Interactions in Public Places

The prevalence of social robots is increasing, with examples such as customer service robots in malls and airports. This trend highlights the importance of transparency, particularly in data-sharing interactions with social robots operating in public spaces, where users may be asked to provide personal information to receive personalized experiences. This article investigates how design transparency influences user trust and data-sharing behavior in human-robot interactions. We conducted an experiment with 143 participants who interacted with the social robot ARI under two transparency conditions: low and high transparency. In the low-transparency condition, participants were informed about the data being collected and could choose to save or delete it. In the high-transparency condition, the robot additionally indicated the sensitivity level of each data item: low (e.g., scenario preference), medium (e.g., name and e-mail), and high (e.g., religious beliefs), allowing participants to make more informed decisions. Participants were presented with two scenarios: exploring city events and discovering local attractions. They received personalized recommendations based on their preferences, with the option to provide personal data (name, phone number, e-mail) for possible future communication. After the interaction, participants decided whether to save or delete the data they had shared. The results indicated that while transparency did not significantly affect trust in the robot, it influenced data-sharing behavior. In particular, participants in the high-transparency condition demonstrated more cautious behavior, opting to save less data and delete more. Furthermore, the results showed that both sensitivity levels and transparency influenced the participants’ data-sharing choices. Low-level sensitivity data led to the highest rates of saving and the lowest rates of deleting, while medium-level sensitivity data showed the opposite pattern. These findings highlight the need to align data categorization with user perceptions to address data sharing concerns more effectively.