A Qualitative Observational Video-Based Study on Perceived Privacy in Social Robots’ Based on Robots Appearances

Privacy has recently got attention, especially since the introduction of the General Data Protection Regulation (GDPR) in Europe, the new Artificial Intelligence Act (AIA) and the new Machinery Regulation. Privacy can be defined as someone’s right to keep personal matters, including personal life, personal information, or relationships, to themselves. A social robot’s appearance (=the combination of embodiment and motion) may contribute to how human users perceive them, including how these robots are perceived in relation to privacy. If these robots are part of certain services such as home- or healthcare, these may also have consequences on how these services are perceived. This study aims at showcasing the users’ perception of privacy based on the perceived robot’s appearance. Three social robots were chosen for this purpose: PLEO (with a zoomorphic appearance), Pepper (with a childlike anthropomorphic appearance), and TIAGo (with a mechanical and asymmetrical appearance). The data was collected through an in-lab observational video-based study from 50 participants with very limited- or no experience with robots. Our findings show that PLEO was perceived as preserving most of the users’ privacy, while Pepper was perceived as more privacy-invasive than PLEO but less than TIAGo. TIAGo was perceived as hard to interpret in terms of privacy. Our findings also point out that designing robots with a cute appearance, such as PLEO, may contribute to participants trusting the robot more and thus being willing to share their data. The paper provides a list of characteristics that participants associated with a social robot as preserving or not their privacy. Further, the paper discusses the appearance of these social robots in terms of “cuteness” as a dark pattern in the design of social robots that may lead to data myopia, but also the possible consequences this may have, for vulnerable users, while trying to design more inclusive robots.