分析大模型生成儿童故事中的文化与性别偏见
Biased Tales: Cultural and Topic Bias in Generating Children's Stories
- 构建数据集分析生成故事中主角属性的偏差
- 女孩角色外貌描述多出55.26%，非西方角色更强调传统
- 揭示社会文化偏见对AI创作公平性的影响
故事在人类交流中起关键作用，尤其影响儿童的价值观。随着家长越来越多依赖大语言模型（LLMs）生成睡前故事，其中存在的文化与性别刻板印象引发严重关切。为此，我们提出Biased Tales，一个全面的数据集，用于分析偏见如何影响LLM生成故事中主角的特征与叙事元素。分析发现：当主角为女孩时，外貌相关属性增加55.26%；非西方儿童的故事显著更强调文化传承、传统与家庭主题，远超西方儿童故事。研究凸显了社会文化偏见在提升创意AI使用公平性与多样性中的关键作用。
原文摘要 · Abstract (English)
Stories play a pivotal role in human communication, shaping beliefs and morals, particularly in children. As parents increasingly rely on large language models (LLMs) to craft bedtime stories, the presence of cultural and gender stereotypes in these narratives raises significant concerns. To address this issue, we present Biased Tales, a comprehensive dataset designed to analyze how biases influence protagonists' attributes and story elements in LLM-generated stories. Our analysis uncovers striking disparities. When the protagonist is described as a girl (as compared to a boy), appearance-related attributes increase by 55.26%. Stories featuring non-Western children disproportionately emphasize cultural heritage, tradition, and family themes far more than those for Western children. Our findings highlight the role of sociocultural bias in making creative AI use more equitable and diverse.
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