揭示AI音乐生成中的全球不平等，发现低资源地区音乐质量显著偏低
Bias beyond Borders: Global Inequalities in AI-Generated Music
- 构建跨国家、跨语言的大型音乐生成数据集GlobalDISCO
- 高资源与低资源地区音乐质量差距明显，主流与小众流派表现差异大
- 适合关注AI公平性、跨文化生成的研究者和音乐科技从业者
尽管近年来音乐生成模型取得显著进展，但其在不同国家、语言、文化和音乐流派间的偏差研究仍不足。这一空白源于缺乏能反映全球音乐多样性的数据集与基准。为此，我们提出GlobalDISCO，一个包含73,000首由先进商业音乐生成模型创作的曲目，并配有93,000个来自LAION-DISCO-12M的参考曲目链接。该数据集覆盖147种语言，涵盖来自79个国家和五大洲的音乐风格，基于MusicBrainz和Wikipedia提取风格提示。评估显示，高资源与低资源地区间音乐质量及与参考曲目的对齐度存在显著差异；同时，主流与地理小众流派间模型性能差异明显，部分情况下模型生成的区域风格更接近主流分布。
原文摘要 · Abstract (English)
While recent years have seen remarkable progress in music generation models, research on their biases across countries, languages, cultures, and musical genres remains underexplored. This gap is compounded by the lack of datasets and benchmarks that capture the global diversity of music. To address these challenges, we introduce GlobalDISCO, a large-scale dataset consisting of 73k music tracks generated by state-of-the-art commercial generative music models, along with paired links to 93k reference tracks in LAION-DISCO-12M. The dataset spans 147 languages and includes musical style prompts extracted from MusicBrainz and Wikipedia. The dataset is globally balanced, representing musical styles from artists across 79 countries and five continents. Our evaluation reveals large disparities in music quality and alignment with reference music between high-resource and low-resource regions. Furthermore, we find marked differences in model performance between mainstream and geographically niche genres, including cases where models generate music for regional genres that more closely align with the distribution of mainstream styles.
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