Large language models exhibit speciesist bias against animals

Abstract We investigate whether large language models (LLMs) exhibit speciesist bias—discrimination based on species membership—and how they value non-human animals. We use three paradigms: SpeciesismBench, a 1009-item benchmark we developed to assess detection and ethical classification of speciesist statements; established psychological measures comparing model and human responses; and text-generation tasks testing for speciesist rationalizations. LLMs reliably detected speciesist statements but often classified them as morally acceptable. On psychological measures, LLMs less frequently than people explicitly respond that animals matter less, yet more strongly prioritized saving one human over multiple animals in concrete dilemmas, a preference that disappeared when humans and animals were matched on cognitive capacity. In text generation, LLM responses repeatedly normalized harm toward farmed animals while refusing to do so for non-farmed animals. These findings show that LLMs encode cultural norms of animal exploitation, suggesting AI fairness frameworks should include non-human moral patients. Acknowledgements TH was supported by the Ministry of Science, Research, and the Arts Baden-Württemberg under Az. 33-7533-9-19/54/5 in Reflecting Intelligent Systems for Diversity, Demography and Democracy (IRIS3D) as well as the Interchange Forum for Reflecting on Intelligent Systems (IRIS) at the University of Stuttgart. DAB was supported by a Harvard Graduate School of Arts and Sciences Prize Fellowship. Thanks to Peter S. Park, Francesca Carlon, Anietta Weckauff, Maluna Menke, Adrià Moret, and Arturs Kanepajs for their comments on and help with the manuscript. Funding Open Access funding enabled and organized by Projekt DEAL. Author information Authors and Affiliations Corresponding authors Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. About this article Cite this article Jotautaitė, M., Caviola, L., Brewster, D.A. et al. Large language models exhibit speciesist bias against animals. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72297-9 Received: Accepted: Published: DOI: https://doi.org/10.1038/s41467-026-72297-9