分析AI生成故事中的性别叙事偏见，揭示隐性偏见的存在
A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories
- 基于叙事结构理论设计提示，系统考察角色设定与情节发展
- 发现不同AI模型均存在性别角色刻板印象，尤其体现在心理描写与行动模式上
- 适合关注AI伦理、内容安全与叙事公平性的研究者与开发者
本文研究ChatGPT、Gemini与Claude生成故事中的性别叙事偏见。提示设计参考普洛普的角色分类与弗莱塔格的叙事结构。通过细读方法，分析故事对提示的遵循程度、角色性别分布、外貌与心理描述、行为表现，以及情节发展与人物关系。结果表明，生成故事中仍存在显著的性别偏见，尤其是隐性偏见，强调需采用解释性方法在多个层面评估此类偏见。
原文摘要 · Abstract (English)
The paper explores the study of gender-based narrative biases in stories generated by ChatGPT, Gemini, and Claude. The prompt design draws on Propp's character classifications and Freytag's narrative structure. The stories are analyzed through a close reading approach, with particular attention to adherence to the prompt, gender distribution of characters, physical and psychological descriptions, actions, and finally, plot development and character relationships. The results reveal the persistence of biases - especially implicit ones - in the generated stories and highlight the importance of assessing biases at multiple levels using an interpretative approach.
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