Year: 2025
Type: article
Abstract: This paper examines the ethical implications and risks of applying artificial intelligence (AI) to Indigenous musical heritage, with a specific focus on the Mijikenda communities in the coast of Kenya. Traditional AI tools, deeply rooted in Western systems of knowledge, carry a significant risk of perpetuating digital colonialism. This manifests through the decontextualisation of sacred musical practices and the infringement of crucial data sovereignty rights. The study aims to analyse the fundamental effects emerging between conventional AI methodologies and the knowledge systems underpinning Mijikenda music. Furthermore, it seeks to identify cases of algorithmic bias and cultural appropriation that may arise from such applications. In response, the paper proposes a decolonial framework to guide the ethical development and deployment of AI within the field of African musicology. Employing a systematic review of existing literature, in line with documented perspectives of Mijikenda practitioners and a complete analysis of scholarly works, the research uncovers a significant divergence. The drawing apart forces exist between AI's often extractive logic and the holistic principles of Indigenous Knowledge Systems (IKS). A primary concern among Mijikenda communities is the widespread threat of cultural appropriation, alongside a strong emphasis on understanding music's core value beyond mere data reliant the paper concludes by advocating for a fundamental paradigm shift: moving away from AI as a tool for extraction towards its utilisation as a platform for community-led cultural care. This transformative approach necessitates grounding AI development firmly within the principles of IKS and decolonial theory, ultimately empowering Indigenous communities and ensuring the sustainable continuity of their living sonic heritage.
Authors: Linus Wechuli Odeke, Amon Kipyegon Kirui
Institution: Kabarak University
Language: English
Subfield: Music
Field: Arts and Humanities
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7117538985