Benchmarking LLM Privacy Recognition for Social Robot Decision Making

• URL: http://arxiv.org/abs/2507.16124v1
• Date: Tue, 22 Jul 2025 00:36:59 GMT
• Title: Benchmarking LLM Privacy Recognition for Social Robot Decision Making
• Authors: Dakota Sullivan, Shirley Zhang, Jennica Li, Heather Kirkorian, Bilge Mutlu, Kassem Fawaz,
• Abstract summary: Social robots are embodied agents that interact with people while following human communication norms.<n>In this study, we aim to explore the extent to which out-of-the-box LLMs are privacy-aware in the context of household social robots.
• Score: 15.763528324946005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Social robots are embodied agents that interact with people while following human communication norms. These robots interact using verbal and non-verbal cues, and share the physical environments of people. While social robots have previously utilized rule-based systems or probabilistic models for user interaction, the rapid evolution of large language models (LLMs) presents new opportunities to develop LLM-empowered social robots for enhanced human-robot interaction. To fully realize these capabilities, however, robots need to collect data such as audio, fine-grained images, video, and locations. As a result, LLMs often process sensitive personal information, particularly within home environments. Given the tension between utility and privacy risks, evaluating how current LLMs manage sensitive data is critical. Specifically, we aim to explore the extent to which out-of-the-box LLMs are privacy-aware in the context of household social robots. In this study, we present a set of privacy-relevant scenarios crafted through the lens of Contextual Integrity (CI). We first survey users' privacy preferences regarding in-home social robot behaviors and then examine how their privacy orientation affects their choices of these behaviors (N = 450). We then provide the same set of scenarios and questions to state-of-the-art LLMs (N = 10) and find that the agreement between humans and LLMs is low. To further investigate the capabilities of LLMs as a potential privacy controller, we implement four additional prompting strategies and compare their results. Finally, we discuss the implications and potential of AI privacy awareness in human-robot interaction.

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