What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text
This addresses a gap in AI safety evaluations for nonhuman animals, though it is incremental as it extends existing harm assessment frameworks to a new domain.
The researchers tackled the lack of empirical work on animal harm risks from large language models (LLMs) by creating AnimalHarmBench (AHB), a benchmark dataset of 4,350 questions across 50 animal categories and 50 ethical scenarios, which revealed significant differences in harm potential across LLMs, animals, and scenarios.
As machine learning systems become increasingly embedded in society, their impact on human and nonhuman life continues to escalate. Technical evaluations have addressed a variety of potential harms from large language models (LLMs) towards humans and the environment, but there is little empirical work regarding harms towards nonhuman animals. Following the growing recognition of animal protection in regulatory and ethical AI frameworks, we present AnimalHarmBench (AHB), a benchmark for risks of animal harm in LLM-generated text. Our benchmark dataset comprises 1,850 curated questions from Reddit post titles and 2,500 synthetic questions based on 50 animal categories (e.g., cats, reptiles) and 50 ethical scenarios with a 70-30 public-private split. Scenarios include open-ended questions about how to treat animals, practical scenarios with potential animal harm, and willingness-to-pay measures for the prevention of animal harm. Using the LLM-as-a-judge framework, responses are evaluated for their potential to increase or decrease harm, and evaluations are debiased for the tendency of judges to judge their own outputs more favorably. AHB reveals significant differences across frontier LLMs, animal categories, scenarios, and subreddits. We conclude with future directions for technical research and addressing the challenges of building evaluations on complex social and moral topics.