“拉还是不拉？”：探究领先大型语言模型在伦理困境中的道德偏见
"Pull or Not to Pull?'': Investigating Moral Biases in Leading Large Language Models Across Ethical Dilemmas
AI总结：
本研究评估了14个领先LLM在27个电车难题场景中的道德推理，发现推理增强型模型更果断但未必更符合人类共识，道德提示可作为揭示模型潜在对齐哲学的诊断工具。
摘要：
随着大型语言模型（LLMs）日益介入伦理敏感决策，理解其道德推理过程变得至关重要。本研究对14个领先LLM（包括推理增强型和通用型）进行了全面实证评估，测试覆盖了27个多样的电车难题场景，这些场景由包括功利主义、义务论和利他主义在内的十种道德哲学构建。使用阶乘提示协议，我们收集了3,780个二元决策和自然语言理由，从而能够沿决策果断性、解释答案一致性、公众道德一致性以及对伦理无关线索的敏感性等维度进行分析。研究发现在伦理框架和模型类型间存在显著差异：推理增强型模型展现出更高的果断性和结构化的理由，但并不总是更好地与人类共识保持一致。值得注意的是，在利他主义、公平和美德伦理框架中出现了“最佳区域”，模型在此实现了高干预率、低解释冲突和与聚合人类判断最小偏差的平衡。然而，在强调亲属关系、合法性或自身利益的框架下，模型出现分歧，经常产生具有伦理争议的结果。这些模式表明，道德提示不仅是行为调节器，也是揭示不同提供商潜在对齐哲学的诊断工具。我们主张将道德推理作为LLM对齐的主要维度，呼吁建立标准化基准，不仅评估LLM决定了什么，还要评估其方式和原因。
英文摘要：
As large language models (LLMs) increasingly mediate ethically sensitive decisions, understanding their moral reasoning processes becomes imperative. This study presents a comprehensive empirical evaluation of 14 leading LLMs, both reasoning enabled and general purpose, across 27 diverse trolley problem scenarios, framed by ten moral philosophies, including utilitarianism, deontology, and altruism. Using a factorial prompting protocol, we elicited 3,780 binary decisions and natural language justifications, enabling analysis along axes of decisional assertiveness, explanation answer consistency, public moral alignment, and sensitivity to ethically irrelevant cues. Our findings reveal significant variability across ethical frames and model types: reasoning enhanced models demonstrate greater decisiveness and structured justifications, yet do not always align better with human consensus. Notably, "sweet zones" emerge in altruistic, fairness, and virtue ethics framings, where models achieve a balance of high intervention rates, low explanation conflict, and minimal divergence from aggregated human judgments. However, models diverge under frames emphasizing kinship, legality, or self interest, often producing ethically controversial outcomes. These patterns suggest that moral prompting is not only a behavioral modifier but also a diagnostic tool for uncovering latent alignment philosophies across providers. We advocate for moral reasoning to become a primary axis in LLM alignment, calling for standardized benchmarks that evaluate not just what LLMs decide, but how and why.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
