mlsanityData to 14 Oct 2026

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

arXiv 2508.09651 ↗First posted 13 Aug 2025cs.HC cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 13 Aug 2025.

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References · 16

  1. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  3. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 207
  4. 2023Gender bias and stereotypes in Large Language Modelscited by 187
  5. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
  6. 2023A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writingcited by 99
  7. 2023A Survey on Fairness in Large Language Modelscited by 95
  8. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 58
  9. 2024Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Modelscited by 33
  10. 2021Uncovering Implicit Gender Bias in Narratives through Commonsense Inferencecited by 32
  11. 2024A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directionscited by 27
  12. 2023Fairness-Aware Structured Pruning in Transformerscited by 22
4 more
  1. 2020Analyzing Gender Bias within Narrative Tropescited by 7
  2. 2024Assessing Gender Bias in LLMs: Comparing LLM Outputs with Human Perceptions and Official Statisticscited by 4
  3. 2024Evaluating Gender Bias in Large Language Modelscited by 2
  4. 2024Gender Biases in LLMs: Higher intelligence in LLM does not necessarily solve gender bias and stereotypingcited by 1

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