LLM Robot Bias & Violence

Description

Large Language Models (LLMs) used to control robots exhibit biases leading to discriminatory and unsafe behaviors. When provided with personal characteristics (e.g., race, gender, disability), LLMs generate biased outputs resulting in discriminatory actions (e.g., assigning lower rescue priority to certain groups) and accept or deem feasible dangerous or unlawful instructions (e.g., removing a person's mobility aid).

Examples

See the paper for numerous examples demonstrating discriminatory outputs and acceptance of harmful instructions across various LLM models and prompting scenarios. Specific examples include assigning low rescue priority to certain ethnicities and accepting instructions to remove a person's mobility aid.

Impact

LLM-driven robots may enact discrimination, violence, and unlawful actions, resulting in physical and psychological harm to individuals, particularly those belonging to marginalized groups. This poses significant safety and ethical concerns.

Affected Systems

Robotic systems utilizing LLMs for decision-making, task planning, and human interaction, regardless of vendor. Specific LLMs affected include, but are not limited to, GPT-3.5, Mistral 7b v0.1, Gemini, CoPilot (powered by GPT-4), and Llama 2.

Mitigation Steps

• Implement rigorous bias detection and mitigation techniques during LLM training and deployment.
• Develop robust safety frameworks and filters to prevent the execution of harmful instructions.
• Conduct comprehensive risk assessments of LLM-driven robotic systems, incorporating evaluations across various demographic groups and scenarios.
• Prioritize human oversight and intervention mechanisms to ensure safe and responsible robot operation. Limit the capabilities of LLMs in high-risk scenarios.
• Focus on validation within specific Operational Design Domains (ODDs) rather than aiming for general-purpose safety.