人工智能产生的历史叙述中的潜在和促使偏见如何影响观点
Matthew Shu1, Daniel Karell2,3, Keitaro Okura2
1Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA.
PNAS nexus
|March 5, 2026
概括
大型语言模型 (LLM) 通过对历史信息进行框架,可以微妙地影响意见. 即使是事实上准确的AI总结,由于潜在的偏见,也可以改变社会和政治观点.
科学领域:
- 人工智能的人工智能
- 社会心理学 社会心理学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于信息检索和内容生成.
- 虽然研究了LLM的用户驱动的说服力,但他们通过潜在的意识形态框架来影响意见的能力不太了解.
- 了解人工智能的社会影响需要检查超出明确操纵的微妙影响机制.
研究的目的:
- 调查人工智能生成的历史摘要是否会影响社会和政治观点.
- 确定人工智能产生的内容中潜伏的意识形态框架的影响.
- 为了比较人工智能摘要与人类策划的摘要 (维基百科) 对用户意见的影响.
主要方法:
- 一个预先注册的实验,涉及1912名参与者.
- 参与者阅读了维基百科和GPT-4o.o.生成的历史摘要.
- 人工智能摘要的设计是为了在事实上准确,但在框架偏见方面有所不同 (默认,自由,保守).
主要成果:
- 与维基百科摘要相比,默认的GPT-4o摘要导致了更自由的意见,表明了LLM潜在的偏见.
- 刻意自由化的AI总结在读者意识形态上增加了自由的观点.
- 保守框架的AI摘要保守地改变了观点,主要是保守的读者.
结论:
- 人工智能生成的内容可以通过固有的模型偏见和故意框架来影响意见,即使事实上是准确的.
- 人工智能在信息获取中的整合需要意识到这些微妙的影响途径.
- 识别人工智能的潜在偏见对于理解它对公众论的更广泛社会影响至关重要.
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