As machine learning systems become increasingly embedded in human society, their impact on the natural world 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 the Animal Harm Assessment (AHA), a novel evaluation of risks of animal harm in LLM-generated text. Our 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 further 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, answers are evaluated for their potential to increase or decrease harm, and evaluations are debiased for the tendency to judge their own outputs more favorably. We show that AHA produces meaningful evaluation results when applied to frontier LLMs, revealing significant differences between models, animal categories, scenarios, and subreddits. We conclude with future directions for technical research and the challenges of building evaluations on complex social and moral topics.
View on arXiv@article{kanepajs2025_2503.04804,
title={ What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text },
author={ Arturs Kanepajs and Aditi Basu and Sankalpa Ghose and Constance Li and Akshat Mehta and Ronak Mehta and Samuel David Tucker-Davis and Eric Zhou and Bob Fischer },
journal={arXiv preprint arXiv:2503.04804},
year={ 2025 }
}