Benchmarking LLM Privacy Recognition for Social Robot Decision Making

TL;DR

This paper evaluates how well current large language models recognize and handle privacy concerns in household robots, highlighting gaps between human privacy preferences and LLM responses.

Contribution

It introduces privacy-relevant scenarios based on user surveys, assesses LLMs' privacy awareness, and compares different prompting strategies for privacy control in social robots.

Findings

Low agreement between humans and LLMs on privacy preferences.

Prompting strategies influence LLMs' privacy responses.

LLMs currently lack robust privacy awareness in HRI contexts.

Abstract

While 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-powered robots for enhanced human-robot interaction (HRI). 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 private environments, such as homes. 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 robots. In this work, we present a set of privacy-relevant scenarios developed using the Contextual Integrity (CI) framework. We first surveyed users' privacy preferences regarding in-home robot behaviors and then examined how their privacy orientations affected their choices of these behaviors (N = 450). We then provided the same set of scenarios and questions to state-of-the-art LLMs (N = 10) and found that the agreement between humans and LLMs was generally low. To further investigate the capabilities of LLMs as potential privacy controllers, we implemented four additional prompting strategies and compared their results. We discuss the performance of the evaluated models as well as the implications and potential of AI privacy awareness in human-robot interaction.