A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories

--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. In recent years, considerable attention has been paid to addressing the problem of bias in Large Language Models (LLMs). Despite ongoing efforts and improvements, LLMs still often do not adequately represent diversity and continue to propagate various forms of societal bias in their output [1] [2] [3]. The extensive use of LLMs for content creation and text generation makes this issue increasingly urgent. Regarding gender bias, studies have explored different aspects, such as the correlation between gender and occupation [4] [5], personas [6] [7], or the use of adjectives [8]. Many of these studies also compared LLMs' correlations with official social data on occupation and human perceptions [5] [9]. Methodologies for studying bias in LLMs can be divided into intrinsic and extrinsic approaches [10] [11]. The intrinsic approach includes embedding-and probability-based bias, while the extrinsic approach focuses on generation-based bias [12]. A recent study from UNESCO [13] provides a comprehensive application of various approaches by studying the connection of gendered words, asking LLMs to complete sentences, and generating entire stories. There are different modes of gender bias and stereotype propagation, and it is important to evaluate the issue from various points of view.