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Privacy and Transparency in Human-Robot Conversations: Effects on Self-Disclosure

Xiyun Zhong,Romain Maure,Barbara Bruno

Published 2025 in IEEE International Symposium on Robot and Human Interactive Communication

ABSTRACT

As social robots increasingly integrate into human society, their ability to sense our surroundings legitimately raises privacy concerns. The objective of this study is twofold. First, we explore the possibility of providing social robots with privacy-preserving sensing, i.e. the ability of extracting necessary sensory information while preserving users’ privacy. Second, we investigate whether the use of such privacy-preserving sensing as well as transparency with respect to the robot’s sensing capabilities can encourage individuals to self-disclose during human-robot conversations. A 2 × 2 between-subject experiment was conducted with 28 participants, who engaged in a conversation with the PixelBot robot. The results suggest that conversational robots can perform effective privacy-preserving feature extraction during interactions with people, but no statistically significant effects were found between the use of such privacy-preserving sensing, nor the robot’s transparency, and the breadth and depth of self-disclosure.

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