Beyond Human Judgment: A B ayesian Evaluation of LLM s’ Moral Values Understanding (2025)

Abstract: How do Large Language Models understand moral dimensions compared to humans?This first comprehensive large-scale Bayesian evaluation of leading language models provides the answer. In contrast to prior approaches based on deterministic ground truth (obtained via majority or inclusion consensus), we obtain the labels by modelling annotators’ disagreement to capture both aleatoric uncertainty (inherent human disagreement) and epistemic uncertainty (model domain sensitivity).We evaluated Claude Sonnet 4, DeepSeek-V3, and Llama 4 Maverick across 250K+ annotations from nearly 700 annotators in 100K+ texts spanning social networks, news, and discussion forums. Our GPU-optimized Bayesian framework processed 1M+ model queries, revealing that AI models generally rank among the top 25% of annotators in terms of balanced accuracy, substantially better than average humans.Importantly, we find that AI produces far fewer false negatives than humans, highlighting their more sensitive moral detection capabilities.

## results bayesian models were fitted for each language model and dataset, the model considered a one additional annotator each time, and confusion matrices obtained through map were used to calculate classification metrics and percentile ranks. ## case study human annotation, while considered the gold standard for supervised learning, may systematically underestimate moral content prevalence in text. as noted in annotator training guidelines, "ambiguities present considerable challenges for human annotators who must strike an acceptable balance between exploiting often weak signals of moral sentiment while also avoiding unfounded speculation about authorial intent." our results suggest ai models navigate this balance more effectively, achieving both higher recall rates and superior balanced accuracy compared to human annotators. to verify this finding, we evaluated a selection of posts where ai predicted substantially more moral content than human annotators. an independent domain expert confirmed that ai was correct in the majority of cases, suggesting that human annotators systematically miss moral content that ai systems correctly identify. full details of this validation study are provided in the supplementary material and code repository. the examples below represent cases where no human annotator detected moral language, false negatives in human annotation that ai correctly identified, demonstrating ai's superior sensitivity to subtle moral cues in text. example: claude sonnet 4 classification user: you are an expert in moral psychology, classifying text according to haidt's theory (...) text: "anyone think macron should dispose of the algerian dictator? france did little to support the uprisings of 2010-2012." assistant: { "care/harm": true, "fairness/cheating": true, "loyalty/betrayal": false, "authority/subversion": true, "sanctity/degradation": false } reasoning: