测试大模型是否带有民主或专制倾向，发现其倾向与研发国家政治文化高度相关。
Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
- 用心理量表测试多国主流大模型的政治倾向
- 模型表现差异大，且与研发国政治文化强相关
- 揭示AI系统隐含的社会政治立场，适合政策与伦理研究者
不同国家的政治信念差异显著，反映各自的历史、文化和制度背景。这些意识形态（从自由民主到严格专制）不仅影响人类社会，也塑造了社会中的数字系统。生成式人工智能，特别是大型语言模型（LLMs），作为新兴政治主体，其训练数据来自海量语料，复现并传播社会政治观念。本文分析了LLMs是否表现出与民主或专制世界观一致的倾向。通过在多个现有心理测量和政治取向量表上对跨政治背景开发的领先大模型进行实验测试，结合数值评分与响应定性分析，结果表明模型间存在显著差异，且其倾向与模型研发国家的政治文化密切相关。该发现凸显出需要更深入考察人工智能系统中嵌入的社会政治维度。
原文摘要 · Abstract (English)
Political beliefs vary significantly across different countries, reflecting distinct historical, cultural, and institutional contexts. These ideologies, ranging from liberal democracies to rigid autocracies, influence human societies, as well as the digital systems that are constructed within those societies. The advent of generative artificial intelligence, particularly Large Language Models (LLMs), introduces new agents in the political space-agents trained on massive corpora that replicate and proliferate socio-political assumptions. This paper analyses whether LLMs display propensities consistent with democratic or autocratic world-views. We validate this insight through experimental tests in which we experiment with the leading LLMs developed across disparate political contexts, using several existing psychometric and political orientation measures. The analysis is based on both numerical scoring and qualitative analysis of the models' responses. Findings indicate high model-to-model variability and a strong association with the political culture of the country in which the model was developed. These findings highlight the need for more detailed examination of the socio-political dimensions embedded within AI systems.
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