Perspectives of Artificial Intelligence in the Cuban Audiovisual Industry: Technological Sovereignty Analyzed through Neutrosophic PEST-SWOT
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Abstract
This article addresses the need to design a public policy aimed at regulating the use of Artificial Intelligence (AI) in the Cuban audiovisual industry, considering technological sovereignty through a neutrosophic PEST-SWOT analysis. The research is based on a theoretical-contextual study examining the historical evolution of AI, its scientific and technological foundations, associated ethical and regulatory aspects, international experiences, and Cuba's particular situation, characterized by technological barriers, limited training, and the absence of a specific regulatory framework. Methodologically, the study adopts a mixed and triangulated approach, integrating theoretical and empirical perspectives with the neutrosophic PEST-SWOT analysis. This procedure allows the systematization of findings by assigning degrees of truth, indeterminacy, and falsity to both the PEST components—Political, Economic, Social, Technological, Environmental, and Legal—and the SWOT elements—Strengths, Weaknesses, Opportunities, and Threats. The final outcome is the proposal of a public policy accompanied by an operational protocol to facilitate ethical, regulated, and effective AI use in radio and television media. This approach not only aims to enhance national development of audiovisual solutions but also contributes to the regional debate on technological sovereignty and digital ethics, establishing clear criteria for the responsible implementation of AI in the audiovisual sector.
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Perspectives of Artificial Intelligence in the Cuban Audiovisual Industry: Technological Sovereignty Analyzed through Neutrosophic PEST-SWOT. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 50-69. https://fs.unm.edu/NCML2/index.php/112/article/view/834
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How to Cite
Perspectives of Artificial Intelligence in the Cuban Audiovisual Industry: Technological Sovereignty Analyzed through Neutrosophic PEST-SWOT. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 50-69. https://fs.unm.edu/NCML2/index.php/112/article/view/834