As large language models (LLMs) become increasingly integrated into society, their alignment with human morals is crucial. To better understand this alignment, we created a large corpus of human- and LLM-generated responses to various moral scenarios. We found a misalignment between human and LLM moral assessments; although both LLMs and humans tended to reject morally complex utilitarian dilemmas, LLMs were more sensitive to personal framing. We then conducted a quantitative user study involving 230 participants (N=230), who evaluated these responses by determining whether they were AI-generated and assessed their agreement with the responses. Human evaluators preferred LLMs' assessments in moral scenarios, though a systematic anti-AI bias was observed: participants were less likely to agree with judgments they believed to be machine-generated. Statistical and NLP-based analyses revealed subtle linguistic differences in responses, influencing detection and agreement. Overall, our findings highlight the complexities of human-AI perception in morally charged decision-making.

本研究旨在填补人类与大语言模型（LLM）在道德评估上的不一致性这一空白。研究者创建了一个人类与LLM生成的道德情境反应的大型语料库，发现LLM对道德判断的敏感性与人类不同，从而影响到对AI生成内容的接受程度。结果表明，尽管人类偏好LLM在道德场景中的评估，但存在系统性的反AI偏见，影响了评估结果。

道德图灵测试：评估人类与大语言模型在道德决策中的一致性