An Ethical Governance Framework for AI Storytelling in Cultural Narratives

An Ethical Governance Framework for AI Storytelling in Cultural Narratives | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF open-forum An Ethical Governance Framework for AI Storytelling in Cultural Narratives Sara Saravi, Firat Batmaz, Yolandi Burger, Robert Harland, Alis Iacob This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8001362/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper introduces the Right Human-in-the-Loop (R-HiTL) model, arguing that it is not merely a best practice but an ethical imperative for the responsible use of generative AI (GenAI) in cultural contexts. Our central claim is that narrative integrity arises from proactive, structured governance rather than after-the-fact fixes. Using the Named after Nelson (NaN) Podcast Series as a case study, we show that raw AI outputs are often factually unreliable, culturally misaligned, and emotionally unconvincing. We embed subject-matter expertise into the content lifecycle through three targeted ‘repair’ loops, cognitive (fact-checking and source validation), cultural (accent and language authenticity), and affective (tone and narrative resonance), to mitigate techno-linguistic bias, hallucination, and the ''emotional deficit". Across two student workshops, the framework delivered measurable improvements in factual accuracy, cultural authenticity, and audience engagement. Participants rated post-intervention outputs higher and valued the expert feedback loop, providing empirical support for R-HiTL as a practical governance mechanism. Our focus is on institutions and educators integrating AI into teaching and cultural storytelling. However, the same governance needs arise in adjacent domains. We therefore note applicability to the GLAM sector (galleries, libraries, archives and museums) and to community heritage organisations, where responsible AI use similarly depends on context-specific expertise and transparent oversight. Taken together, the findings validate R-HiTL as a workable blueprint for teams seeking to use GenAI responsibly while safeguarding narrative integrity and genuine inclusion. AI Ethics Generative AI Cultural Narratives Right Human-in-the-Loop (R-HiTL) Equality Diversity and Inclusion (EDI) Storytelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8001362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"open-forum","associatedPublications":[],"authors":[{"id":538702887,"identity":"f4c10199-0959-4b2d-b8a4-b515010c5cce","order_by":0,"name":"Sara 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Inclusion (EDI), Storytelling","lastPublishedDoi":"10.21203/rs.3.rs-8001362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8001362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper introduces the Right Human-in-the-Loop (R-HiTL) model, arguing that it is not merely a best practice but an ethical imperative for the responsible use of generative AI (GenAI) in cultural contexts. Our central claim is that narrative integrity arises from proactive, structured governance rather than after-the-fact fixes. Using the Named after Nelson (NaN) Podcast Series as a case study, we show that raw AI outputs are often factually unreliable, culturally misaligned, and emotionally unconvincing. We embed subject-matter expertise into the content lifecycle through three targeted ‘repair’ loops, cognitive (fact-checking and source validation), cultural (accent and language authenticity), and affective (tone and narrative resonance), to mitigate techno-linguistic bias, hallucination, and the ''emotional deficit\". Across two student workshops, the framework delivered measurable improvements in factual accuracy, cultural authenticity, and audience engagement. Participants rated post-intervention outputs higher and valued the expert feedback loop, providing empirical support for R-HiTL as a practical governance mechanism. Our focus is on institutions and educators integrating AI into teaching and cultural storytelling. However, the same governance needs arise in adjacent domains. We therefore note applicability to the GLAM sector (galleries, libraries, archives and museums) and to community heritage organisations, where responsible AI use similarly depends on context-specific expertise and transparent oversight. 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