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Large language models exhibit speciesist bias against animals

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Chimera Difficulty Score
a synthesis of Flesch-Kincaid, Coleman-Liau, SMOG, and Dale-Chall readability metrics
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-gene...
The investigation demonstrates that advanced language models do not merely reflect human linguistic patterns but actively encode and operationalize cultural norms regarding animal exploitation. The discrepancy between detection and moral classification highlights a systemic flaw: the models recognize the *form* of speciesist thought but operate within a framework that permits its acceptance, suggesting the bias is embedded in the training data and the underlying architecture rather than being a ...