AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini

• URL: http://arxiv.org/abs/2507.22445v1
• Date: Wed, 30 Jul 2025 07:44:28 GMT
• Title: AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini
• Authors: Jill Walker Rettberg, Hermann Wigers,
• Abstract summary: We generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential demonym story" to OpenAI's model.<n>Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries.<n>The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP research, and efforts to improve the cultural alignment of generative AI.

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