Algorithmic Authorship and Cultural Bias: A Literary Analysis of AI-Generated Narratives

This paper examines algorithmic authorship and cultural bias in AI-generated narratives through qualitative literary analysis. Focusing on three fictional stories produced by a large language model under controlled prompts, the study explores how narrative voice, character agency, authority, and conflict resolution reflect culturally embedded assumptions. The analysis identifies recurring gendered and cultural hierarchies that persist despite attempts to minimise explicit bias, suggesting that AI-generated storytelling reproduces dominant narrative conventions learned from human-authored corpora. By approaching generative outputs as cultural texts rather than neutral technological artefacts, the paper argues for the importance of interpretive methods from the digital humanities in evaluating algorithmic narratives.