比较人工智能和人类道德推理:超越实用偏见的情境敏感模式.
Elyas Barabadi1, Zahra Fotuhabadi1, Amanollah Arghavan2
1Department of Foreign Languages, University of Bojnord, Bojnord, Iran.
Frontiers in artificial intelligence
|January 28, 2026
概括
大型语言模型 (LLM) 展示了情境敏感的道德判断,在伦理学和实用主义选择之间交替. 人工智能的这种细微决策对于社会对道德敏感应用程序的信任至关重要.
科学领域:
- 人工智能伦理学 人工智能伦理学
- 计算道德 计算机道德
- 自然语言处理自然语言处理.
背景情况:
- 智能系统越来越多地用于伦理敏感领域.
- 了解大型语言模型 (LLM) 的道德判断至关重要.
研究的目的:
- 调查ChatGPT和克劳德·索内特的道德判断.
- 为了确定LLM的反应是否与伦理学或功利主义伦理一致.
- 将LLM的道德反应与人类参与者进行比较.
主要方法:
- 对12个道德场景的LLM反应进行系统的调查.
- 将LLM输出 (ChatGPT,Claude Sonnet) 与先前的人类参与者数据进行比较.
- 对道德选择与德伦理与功利主义框架的结合进行分析.
主要成果:
- 法律学士表现出情境敏感的道德判断,而不是固定的功利主义倾向.
- 两种模型都在基于场景特点的德伦理和实用选择之间交替.
- 法律法学士课程的响应模式显示了微妙的分布,而不是单一的伦理方向.
结论:
- 伦理学士道德决策是微妙的,并取决于背景.
- 这些发现影响了社会对AI在敏感领域的信任和接受.
- 需要进一步的研究来理解人工智能的复杂道德权衡.
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