This article investigates gender bias in narratives generated by Large Language Models (LLMs) through a two-phase study. Building on our existing work in narrative generation, we employ a structured methodology to analyze the influence of protagonist gender on both the generation and classification of fictional stories. In Phase 1, factual narratives were generated using six LLMs, guided by predefined narrative structures (Hero's Journey and Heroine's Journey). Gender bias was quantified through specialized metrics and statistical analyses, revealing significant disparities in protagonist gender distribution and associations with narrative archetypes. In Phase 2, counterfactual narratives were constructed by altering the protagonists’ genders while preserving all other narrative elements. These narratives were then classified by the same LLMs to assess how gender influences their interpretation of narrative structures. Results indicate that LLMs exhibit difficulty in disentangling the protagonist's gender from the narrative structure, often using gender as a heuristic to classify stories. Male protagonists in emotionally driven narratives were frequently misclassified as following the Heroine's Journey, while female protagonists in logic-driven conflicts were misclassified as adhering to the Hero's Journey. These findings provide empirical evidence of embedded gender biases in LLM-generated narratives, highlighting the need for bias mitigation strategies in AI-driven storytelling to promote diversity and inclusivity in computational narrative generation.
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There is a need for modernizing the Dutch collective management system of music copyright to match the rapidly changing digital music industry. Focusing on the often-neglected human values aspect, this study, part of a larger PhD research, examines the value preferences of music rights holders: creators and publishers. It aims to advise on technological redesign for music copyright management system and contribute to discussions on equitable collective management. Building upon prior research, which comprehensively analyzed the Dutch music copyright system and identified key stakeholders, this paper analyses 24 interviews with those key stakeholders to identify their values and potential value tensions. Initial findings establish a set of shared values, crucial for the next phases of the study –values operationalization. This research makes a academic contribution by integrating the Value Sensitive Design (VSD) approach with Distributive Justice Theory, enriching VSD's application and enhancing our understanding of the Economics of Collective Management (ECM).
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