Bias beyond Borders: Global Inequalities in AI-Generated Music

2025-10-02

AI Summary

This study reveals systemic biases in AI music generation models across global linguistic, cultural, and stylistic dimensions: dominant styles are overrepresented, while low-resource regional musics are frequently homogenized or inaccurately generated. To address this, we introduce GlobalDISCO—a benchmark dataset spanning 79 countries and 147 languages—and propose the first globally balanced multilingual, multi-style evaluation framework, integrating cross-cultural alignment assessment with quantitative style consistency metrics. Leveraging LAION-DISCO-12M alongside MusicBrainz/Wikipedia metadata, we generate large-scale audio samples for cross-national comparative analysis. Experiments demonstrate significantly higher generation fidelity in high-resource regions versus low-resource ones, confirming structural global inequity in AI music synthesis. Key contributions include: (1) the first globalization-aware music bias benchmark; (2) the first cross-cultural alignment evaluation methodology; and (3) empirical evidence of systematic underrepresentation and distortion of marginalized musical styles.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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.

Problem

• Addressing biases in AI music generation across cultures
• Creating globally balanced dataset for diverse music evaluation
• Identifying quality disparities between mainstream and regional genres

Innovation

• Introduced GlobalDISCO dataset with 73k AI-generated tracks
• Leveraged 147 languages and 79 countries for global representation
• Evaluated disparities between high-resource and low-resource regions