Ethical Frameworks for Responsible Music AI: Balancing Creativity, Ownership, and Cultural Impact

The rapid rise of music AI systems—such as OpenAI’s Jukebox, Suno AI, and virtual ensembles like Mave—has transformed creative workflows, with 60% of musicians now adopting these tools. However, this growth introduces ethical challenges that threaten cultural “place” (local musical identities) and creative “space” (authorship, agency, and equity). AI models trained on Eurocentric datasets (80–85% Western genres) risk appropriating non-Western forms like raga, maqam, or gagaku. Additional concerns include copyright disputes, algorithmic bias favoring 4/4 rhythms, opaque model ar-chitectures, diminished artist agency, environmental costs from large-scale generative models, and deepfake misuse mimicking artists such as Drake and The Weeknd. Economic inequity is also stark, with only 0.4% of musicians earning sustainable streaming income, while human-AI performances pose emerging safety risks. This position paper proposes a comprehensive ethical framework for responsible music AI. It integrates Confucian harmony to support collective creativity, Buddhist compassion to minimize harm, and Shinto animism to encourage respectful AI collaboration. These are aligned with Human-Centered AI (HCAI) principles, technomoral virtues like empathy and honesty, and the UN Sustainable Development Goals. We advocate for blockchain-based attribution to clarify ownership, community-governed datasets with at least 45% non-Western content to ensure cultural authenticity, culturally sensitive design to protect sacred music, transparent metadata standards to counter deepfakes, regular bias audits to promote diversity, and energy-efficient models for sustainability. Case studies—including Tone Transfer’s ethical exclusion of guqin and Suno AI’s legal challenges—underscore the need for frameworks that go beyond UNESCO’s generic guidelines to foster ethically grounded, culturally resonant music ecosystems.