2024/12/17 by Roser Batlle-Roca, Emila Gómez, WeiHsiang Liao +2 · 1 voice
Computer Science · #Music Technology and Sound Studies
paper · pdf · doi:10.21203/rs.3.rs-3708077/v2
crossref issued 2024/12/17 · crossref published 2024/12/17 · openalex publication_date 2024/12/17 · crossref created 2024/12/17 · crossref deposited 2024/12/20 · openalex created_date 2025/10/10 · crossref indexed 2026/02/28 · openalex updated_date 2026/07/14
Abstract Recent advancements in music-generative AI raise ethical, social, legal and economic concerns linked to artists’ work, the existing music industry model, the role of AI in creative processes, and intellectual property rights. Transparency, a pillar for trustworthy AI, is key to addressing the principal ethical implications of generative AI in the music domain. We analyse transparency approaches for generative AI in music with a two-stage systematic literature review, identifying 107 relevant publications. Findings reveal a growing interest in AI transparency and the ethical implications of generative models. Yet, transparent methodologies for music-generative AI remain an under-explored topic, highlighting such research gap and the need to expand research in this direction. To encourage future exploration, we created a dynamic list of relevant publications in a public repository to be updated with new research initiatives.