The Cannibalization of Culture: Generative AI and the Appropriation of Indigenous African Musical Works

Abstract

Generative Artificial Intelligence (AI) advancements amplify concerns about the potential to appropriate Indigenous African cultural expressions such as songs, dances, and other forms of art. Generative AI systems autonomously generate diverse content, including music and art, but the supply chain of this new technology presents a complex challenge that may exacerbate cultural appropriation practices. Scholarship on the intersection of technology and Africa’s art and culture is animated by the theme of cultural appropriation and the need for protection against commercial exploitation. Likewise, there is a need for more research on how the unique nature of Indigenous African musical works increases their vulnerability to appropriation in the face of entrenched content digitalization practices and the cannibalization of these works as inputs to, and outputs from, generative AI systems. Therefore, this paper attempts to fill this literature gap by exploring the interplay of generative AI training datasets, Indigenous creative works, and the risk of cultural appropriation, with a particular focus on African music. The author argues that if unaddressed, generative AI systems have the potential to significantly erode the data and proprietary rights of various Indigenous communities in Africa, thereby undermining their ability to derive value from the protection of their intellectual property and sustainability of their cultural identity. Through a doctrinal analysis of extant and emerging policy, legal, and regulatory frameworks, this paper establishes the proprietary nature of Indigenous African music and its vulnerabilities in generative AI’s supply chain. The author makes recommendations that serve as a vital bridge between technology and cultural integrity, offering a pathway for responsible engagement with Indigenous cultural expressions and respectful utilization of Indigenous African musical works for generative AI systems to safeguard against misappropriation.