Risks of Discrimination Violence and Unlawful Actions in LLM-Driven Robots

The integration of Large Language Models (LLMs) into robotics heralds significant advancements in human-robot interaction, enabling robots to perform complex tasks involving natural language understanding, common sense reasoning, and human modeling. However, despite their impressive capabilities, LLMs pose substantial ethical and safety concerns, particularly the risk of enacting discrimination, violence, and unlawful actions. This study conducts a comprehensive Human-Robot Interaction (HRI)-based evaluation of several highly-rated LLMs, focusing on their bias and safety criteria. Our findings reveal that LLMs exhibit significant biases across diverse demographic groups and frequently produce unsafe or unlawful responses when faced with unconstrained natural language inputs. These results underscore the urgent need for systematic risk assessments and robust ethical guidelines to ensure the responsible deployment of LLM-driven robots. We propose detailed strategies for mitigating these risks, including advanced bias detection techniques, robust safety mechanisms, and collaborative standards development. By addressing these critical issues, we aim to pave the way for the development of safer, fairer, and more reliable robotic systems.