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.
Jotautaite et al. (Sat,) studied this question.