For robots to become integrated into our daily environment, they must be designed to gain sufficient trust of both users and bystanders. This is in particular important for social robots including those that assume the role of a mediator, working towards positively shaping relationships and interactions between individuals. One crucial factor influencing trust is the appropriate handling of personal information. Previous research on privacy has focused on data collection, secure storage, and abstract third-party disclosure risks. However, robot mediators may face situations where the disclosure of private information about one person to another specific person appears necessary. It is not clear if, how, and to what extent robots should share private information between people. This study presents an online investigation into appropriate robotic disclosure strategies. Using a vignette design, participants were presented with written descriptions of situations where a social robot reveals personal information about its owner to support pro-social human-human interaction. Participants were asked to choose the most appropriate robot behaviors, which differed in the level of information disclosure. We aimed to explore the effects of disclosure context, such as the relationship to the other person and the information content. The findings indicate that both the information content and relationship configurations significantly influence the perception of appropriate behavior but are not the sole determinants of disclosure-adequacy perception. The results also suggest that expected benefits of disclosure and individual general privacy attitudes serve as additional influential factors. These insights can inform the design of future mediating robots, enabling them to make more privacy-appropriate decisions which could foster trust and acceptance.
CITATION STYLE
Dietrich, M., Krüger, M., & Weisswange, T. H. (2023). What should a robot disclose about me? A study about privacy-appropriate behaviors for social robots. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1236733
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