Generative artificial intelligence systems not only produce expressive outputs, but also provide rich latent spaces, i.e., mathematical objects that express the semantic relation among the training data. These unique representational strata represent creative associations and redefine the traditional limits of copyright law. The article introduces the concept of Latent Copyrights, which proposes the provision of copyright protection to intermediate representations of the products generated by AI. It examines the use of computational creativity for the purpose of copyright protection. Engaging in a comparative analysis of the copyright regimes of the U.S., U.K., and E.U., this study suggests a relative system to ensure the protection of innovation, interoperability, and responsibility. The aim is to streamline the Latent Copyright theory of intellectual property with the technicalities of machine learning by offering a paradigm that conceptualizes representational layers of an AI-generated product as a medium of Copyright.

This study maps the intellectual structure and thematic evolution of artificial intelligence (AI) in digital marketing through a bibliometric analysis of 827 Scopus-indexed articles (1986–2026). Utilizing Bibliometrix and VOSviewer, performance analysis and science mapping reveal recent, accelerated growth across interdisciplinary outlets. China, the US, and India lead productivity, while Malaysia, Jordan, Australia, and the UK drive international collaboration. Thematic mapping identifies machine learning for commerce and consumer behavior as a dominant motor theme. In contrast, human-centric social media research remains a specialized niche, and generative AI emerges as a fast-growing frontier. Furthermore, co-citation networks expose a three-pillar intellectual foundation rooted in digital transformation, quantitative methodology, and technology adoption behavior. Ultimately, this long-horizon science map highlights strategic opportunities to integrate emerging generative AI and human-centric approaches with established, analytics-driven marketing frameworks.