Social Robots in Healthcare: Characterizing Privacy Considerations

Abstract

As healthcare robots gain traction, human-robot interaction (HRI) researchers are exploring the factors that impact user adoption and trust in these robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility, usefulness, and adoption. In our work, we conducted a 3×3×3 online study (N = 239) to explore peoples' perceptions of privacy and utility of 3 robots at varying levels of Human-Likeness (HL) across 3 realistic healthcare contexts. The results show that the context of care delivery is a key driver of perceptions of privacy and acceptable privacy-utility trade-offs. Interestingly, the HL of robot design may not significantly impact peoples' privacy perceptions of healthcare robots. We plan to leverage these key findings to develop privacy-aware robot behaviors that are context adaptable in order to improve privacy outcomes for healthcare robots.