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大型语言模型(LLMs)如何评估感知到的道德主体性?探究人机交互中的道德决策

How do LLMs Evaluate Perceived Moral Agency? Investigating Moral Decision-Making in Human-Artificial Agents Interactions

Fernanda Mansilla, Aloysius Tok, Bahia Guellaï, Farah Benamara, Nancy F. Chen

arXiv 2609.05037首次发表:更新:

发表机构

IRIT; Université de Toulouse; CLLE; IPAL(图卢兹计算机科学研究所; 图卢兹大学; 语言、文本、话语与认知实验室; 国际感知、代理与学习研究所)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本研究探究LLMs对感知道德主体性的评估,通过对比人类与LLMs在智慧城市情境下对不同主体的道德主体性感知,发现LLMs在道德决策中具情境敏感性,优先考量伤害程度与紧迫性。

AI 中文摘要

随着大型语言模型(LLMs)承担起提供道德建议的角色,理解它们如何赋予道德主体性变得至关重要。人类拥有道德主体性,这是道德心理学中公认的概念,指的是做出符合伦理的决策并为其后果承担责任的能力。然而,当机器人、无人机和无实体人工智能系统等人工主体(AAs)越来越多地融入智慧城市环境时,是否以及如何赋予它们道德主体性的问题变得愈发紧迫。据我们所知,本文开展了首个实证研究,比较人类和LLMs如何在不同具身程度的人类与自主人工主体中评估感知到的道德主体性(PMA),研究场景设定在合理的智慧城市情境中。我们采用经过验证的PMA量表的改编版本,对190名人类参与者及多种LLMs应用了该方案。我们的评估显示,人类的道德主体性感知高于人工主体。但在具体情境中面对道德困境时,LLMs会从情境出发进行推理,优先考虑伤害严重程度和情境紧迫性,而非对主体本身的稳定评估,这放大了人类评估者也存在的情境敏感性。这些发现与LLMs越来越多地参与日常道德决策密切相关。

英文摘要

As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical. Humans possess moral agency, the capacity to make ethically guided decisions and bear responsibility for their consequences, a well-established construct in moral psychology. Yet as artificial agents (AAs) such as robots, drones, and disembodied AI systems become increasingly embedded in smart city environments, the question of whether and how moral agency is attributed to them takes on new urgency. This paper presents, to the best of our knowledge, the first empirical study comparing how humans and LLMs evaluate perceived moral agency (PMA) across human and autonomous artificial agents varying in embodiment, situated in plausible smart city scenarios. Using an adaptation of a validated PMA scale, we applied a protocol to 190 human participants as well as various LLMs. Our evaluation reveals higher perceptions of moral agency in humans than in AAs. However, when facing moral dilemmas in concrete scenarios, LLMs reason outward from the situation, prioritizing harm severity and contextual urgency over any stable assessment of the agent itself, amplifying a context-sensitivity also present in human raters. These findings are particularly relevant as LLMs become increasingly involved in everyday moral decisions.

Comments43 pages, 14 figures, 29 tables. Preprint under review

论文原文

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