Published: 10/5/2026
Source: The AI Musicpreneur
University of Johannesburg researchers argue that Western-heavy AI music systems can flatten distinct African traditions into a generalized sound and marginalize the cultural knowledge carried by those traditions.
The paper is an integrative literature review, not new fieldwork. It cites a 2026 experiment in which Suno and Udio each generated 10 attempts at Kenyan Benga music; sonic analysis and interviews with music experts found that neither system reproduced Benga melodies accurately.
The authors describe the problem as “epistemic injustice”: technology treats Western musical practice as the default while making other systems harder to represent on their own terms. They propose transparent training-data documentation, independent cultural benchmarks, community-consent protocols, collective licensing and support for African communities to build their own models.
AI music creators should treat broad regional prompts with caution. Detailed prompts can improve direction, but they cannot repair missing training data or replace collaboration with musicians who know the tradition. The stronger workflow is culturally informed, licensed and human-guided from the start.