Large Language Models, Entertainment Authenticity, Streaming Discoverability, and Rightsholders’ Diversity in a Cyber Fraud Era: Beyond the Borders of French-Speaking African and Caribbean Countries

Gbadebo Odularu () and Diweng Dafong
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Gbadebo Odularu: Howard University, Department of Economics
Diweng Dafong: The University of Alabama, Community Engagement of Graduate Student Association, Graduate Teaching Assistant, Chair UALC 2025, Department of Modern Languages and Classics

A chapter in Behavioral Cybersecurity and Ethical AI in Relational Economics Context, 2026, pp 359-378 from Springer

Abstract: Abstract The authenticity, information asymmetry and enforcement costs implications of adopting Large Language Models (LLMs)-based content identification (ID) systems in the French-speaking African and Caribbean entertainment industry are significant. This study examines the potential applications of LLM-based content ID systems in content creation, audience engagement, market analysis, and distribution strategies, considering the region's unique linguistic and cultural diversity, as seen in the works of filmmakers such as Amina Weira, Apolline Traoré, and Mati Diop. By analyzing scholarly articles, industry publications, and case studies, this chapter discusses that LLM-based content ID systems deployed in selected French-speaking African Caribbean entertainment industries disproportionately cause harm through wrongful takedowns, misclassification, and skewed royalty distributions, while consolidating the market power of major labels. Absense of ethnocultural and anthropological expertise in dataset assembly and labeling reduces the accuracy and fairness of LLM-content ID systems in African and the Caribbean regions.

Keywords: Large Language Models (LLMs)-based content identification (ID) systems; Declining Rights-holders diversity; Independent Labels; Streaming reductions; Local Content Manipulation; Discoverability; Entertainment; Local Culture Authenticity; Wolof; Creole; zouk; Traore; Diop; Weira; DIT; Digital platforms; underrepresentation; lack of expert curation; Artificial intelligence; Recommendation systems; Platform enforcement; Large language models; epistemica power asymmetries; language modelling bias; systematic underrepresentation; hermeneutical injustices; Public finance; Government policies; Investment; Social implications of technology; Generative AI; Internet; Trademarks; Algorithmic fairness; cultural erasure; misclassification; gwoka; under-documentation; royalty model; Authenticity-Discoverability-Diversity Self-Reinforcing Loop; epistemic injustices (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-01214-2_15

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