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.
@article{arxiv.2508.09651,
title = {A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories},
author = {Daniel Raffini and Agnese Macori and Marco Angelini and Tiziana Catarci},
journal= {arXiv preprint arXiv:2508.09651},
year = {2025}
}