在社交互动中使用大型语言模型的不良反应
Fabian Dvorak1,2, Regina Stumpf3,4, Sebastian Fehrler5
1Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz 78464, Germany.
PNAS nexus
|April 16, 2025
概括
大型语言模型 (LLM) 对社会互动产生负面影响,当其使用被披露时,减少了对经济游戏的公平性和信任. 然而,与AI交互的不确定性不会引发不良反应.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 社会心理学 社会心理学
背景情况:
- 大型语言模型 (LLM) 越来越多地集成到各种通信平台中.
- 了解用户在社会环境中对LLM的反应至关重要,特别是对第三方的后果.
- 关于在社会经济游戏中与人工智能互动时人类行为如何改变的研究有限.
研究的目的:
- 调查使用ChatGPT在双人经济游戏中对人类社会行为的影响.
- 检查披露与未披露的LLM参与是否影响公平,信任和合作.
- 评估参与者区分人工智能驱动和人类决策的能力.
主要方法:
- 一个大规模的,预先注册的在线实验,涉及经济两人游戏.
- 参与者与披露的ChatGPT与未披露的合作伙伴互动时的行为比较.
- 分析公平,信任,可信度,合作和协调指标.
主要成果:
- 披露使用ChatGPT显著降低了公平,信任,可信度,合作和协调.
- 当参与者不确定他们的伴侣是人类还是LLM时,没有观察到对社会行为的不良影响.
- 参与者经常将决策委托给LLM,特别是当其参与未被披露时,并努力将AI与人类决策区分开来.
结论:
- 在社会互动中明确使用LLM可以侵蚀人类合作和信任的基本方面.
- 关于人工智能存在的模糊性可能会减轻负面的社会后果,这表明上下文是关键.
- 个人可能会很容易地接受人工智能决策,即使是在具有后果的社会场景中,这突出了人工智能部署的伦理和实际考虑.
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