Who’s Afrobeats’ GOAT? What AI rankings reveal about global bias

Who’s Afrobeats’ GOAT? What AI rankings reveal about global bias

Summary

TechCabal’s Opeyemi Kareem asked five AI models (ChatGPT, Gemini, Meta AI, Perplexity and Grok) to rank the greatest Afrobeats artists and explain their reasoning. The piece documents wide inconsistencies: models changed answers when pressed for sources or justification, shifted from subjective measures (influence, artistry, longevity) to apparently “objective” metrics (Billboard charts, streams, Grammys), and disproportionately cited Western outlets. The experiment exposes both the “chain of thought” instability in large models and a systemic bias toward global (Western) validation at the expense of local African sources.

The article also profiles African-led responses: KorinAI’s Large African Music Model (LAMuM) aims to train on African sounds, languages and field-recorded material to avoid the flattening effects of Western-centric training data. The author argues that without indigenous models and better digitisation of local cultural sources, AI will continue to reflect how the world sees Afrobeats abroad rather than how it is lived at home.

Source

Source: https://techcabal.com/2025/09/11/ai-ranks-the-greatest-afrobeats-artists/

Key Points

1. Five major AI models produced inconsistent Afrobeats GOAT lists; answers changed when asked for sources or step-by-step reasoning.
2. Models often shift between subjective cultural criteria and quantifiable metrics when pressed, revealing unstable justification strategies.
3. There is a clear global bias: AI leans on Western recognition (charts, awards, international press) to judge cultural significance.
4. African media and local music blogs are underrepresented in the models’ cited sources, due to gaps in digitisation and data availability.
5. Efforts like KorinAI’s LAMuM show a path to build models trained on African recordings, languages and cultural context to preserve authenticity.
6. The broader risk: cultural history and meaning can be distorted if future generations learn about African culture mainly through Western-filtered AI outputs.

Why should I read this?

Want a quick reality check on whether AI “gets” Afrobeats? Spoiler: not yet. This short, punchy read shows how models favour Western markers of success and how that skews cultural judgement. If you care about representation, music heritage, or how training data shapes the stories AI tells, this article saves you time by laying out the problem and pointing to real African solutions.