Moral stereotyping in large language models - Aliah Zewail; Alexandra Figueroa; Jesse Graham; Mohammad Atari (Proceedings of the National Academy of Sciences, 2026)
Can Large Language Models (LLMs) accurately estimate various societies’ moral values? Here, we query the perceptions of LLMs regarding the moral norms of the “average” person from 48 nations and compare them to a large-scale ( n = 90 , 802 ) survey of six moral values (Care, Equality, Proportionality, Loyalty, Authority, and Purity) from those populations. Our findings indicate that LLMs poorly capture the moral diversity around the globe, systematically overestimating some moral values (particularly Care) and underestimating others (especially Purity). Notably, examining various versions of Generative Pre-trained Transformer (GPT) shows that these LLMs may overestimate the overall moral concerns of some Western countries (e.g., the United States, Canada, and Australia) while underestimating those of non-Western countries (e.g., Nigeria, Morocco, and Indonesia). Our work demonstrates that LLMs are inaccurate generators of cross-cultural estimations in the moral domain; in other words, they stereotype the moral values of non-Western populations in predictable ways. Our results highlight the ethical and epistemic risks of relying on LLMs to estimate the endorsement of moral values around the globe.