Transparency in Music-Generative AI: A Systematic Literature Review

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.