Large Language Models (LLMs) and search engines have the potential to perpetuate biases and stereotypes by amplifying existing prejudices in their training data and algorithmic processes, thereby influencing public perception and decision-making. While most work has focused on Western-centric AI technologies, we study Chinese-based tools by investigating social biases embedded in the major Chinese search engine, Baidu, and two leading LLMs, Ernie and Qwen. Leveraging a dataset of 240 social groups across 13 categories describing Chinese society, we collect over 30k views encoded in the aforementioned tools by prompting them for candidate words describing such groups. We find that language models exhibit a larger variety of embedded views compared to the search engine, although Baidu and Qwen generate negative content more often than Ernie. We also find a moderate prevalence of stereotypes embedded in the language models, many of which potentially promote offensive and derogatory views. Our work highlights the importance of promoting fairness and inclusivity in AI technologies with a global perspective.

翻译：大型语言模型（LLMs）与搜索引擎可能通过放大其训练数据和算法过程中已有的偏见，延续社会偏见与刻板印象，从而影响公众认知与决策。尽管现有研究大多聚焦于以西方为中心的人工智能技术，本文通过考察主流中文搜索引擎百度及两个领先的大型语言模型Ernie与Qwen中嵌入的社会偏见，对中文人工智能工具展开研究。基于涵盖中国社会13个类别共240个社会群体的数据集，我们通过提示上述工具生成描述这些群体的候选词汇，收集了超过3万条隐含观点。研究发现，与搜索引擎相比，语言模型展现出更丰富的观点多样性，但百度与Qwen生成负面内容的频率高于Ernie。同时，我们观察到语言模型中存在一定程度的刻板印象嵌入，其中许多可能助长冒犯性与贬损性观点。本研究凸显了以全球视野推动人工智能技术公平性与包容性的重要意义。