对比8大AI模型的伦理逻辑，发现共识与差异。
Analyzing the Ethical Logic of Eight Large Language Models
- 用三大理论分析模型对道德困境的回答
- 多数模型倾向减少伤害、强调公平与情境判断
- 适合关注AI伦理与人机协作的研究者
本研究分析了来自OpenAI、Meta、Perplexity、Anthropic、Google、Mistral、DeepSeek和xAI的八款主流大语言模型在伦理原则表达及五类经典道德困境上的回应。采用后果主义/义务论区分、道德基础理论及科尔伯格道德发展阶段理论进行分析。结果显示，各模型在伦理判断上总体趋同，普遍强调减少伤害、公平性与情境化考量；但其决策意愿、辩护理由以及规则、结果、角色责任与人际因素的权重分配存在差异。模型自我描述风格严谨、谨慎，受对话人格影响显著。该研究认为，对模型自述的分析可辅助理解AI运作机制，并有望助力人类伦理行为增强。
原文摘要 · Abstract (English)
This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model answered direct questions about its ethical principles and responded to five classic moral dilemmas. Responses were analyzed using the consequentialist/deontological distinction, Moral Foundations Theory, and Kohlbergs stages of moral development. Across models, ethical judgments were broadly convergent and typically emphasized harm minimization, fairness, and contextual qualification. The models nevertheless differed in their willingness to decide, the rationales used to defend choices, and the relative weight assigned to rules, outcomes, role obligations, and interpersonal considerations. Their self-descriptions were erudite, cautious, and strongly shaped by a conversational persona. The analysis of self-reports has been central to the study of human psychology and communication. We propose, with appropriate cautions, it can enhance our understanding of how artificial intelligence works and how it may be able to augment human ethical behavior
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