揭示音乐AI中的文化偏见，呼吁公平性改进
Who Gets Heard? Rethinking Fairness in AI for Music Systems
- 分析音乐AI系统中的文化与流派偏见来源
- 发现全球南方传统音乐常被误表达，导致真实性受损
- 提出数据、模型、界面三层面的公平性改进建议
近年来，音乐研究界关注生成式AI在音乐领域的风险，如版权问题、深度伪造和透明度不足。本文聚焦音乐AI系统中的文化与流派偏见，这些偏见影响创作者、发行方和听众，对代表性产生负面影响。尤其对全球南方的边缘化音乐传统，易造成失真表现（如扭曲的拉加），降低创作者信任。此类偏见可能强化既有不公，限制创作自由，加剧文化消亡。为此，本文从数据集、模型设计和交互界面三个层面提出改进建议。
原文摘要 · Abstract (English)
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 for music. These biases can misrepresent marginalized traditions, especially from the Global South, producing inauthentic outputs (e.g., distorted ragas) that reduces creators' trust on these systems. Such harms risk reinforcing biases, limiting creativity, and contributing to cultural erasure. To address this, we offer recommendations at dataset, model and interface level in music-AI systems.
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