MusGO: A Community-Driven Framework for Assessing Openness in Music-Generative AI

Since 2023, generative AI has rapidly advanced in the music domain. Despite significant technological outcomes, music-generative models raise critical ethical challenges, including the lack of transparency, accountability, and risks like the possible replication of artists' works, which highlights the importance of fostering openness. With upcoming regulations such as the EU AI Act encouraging open models, many generative models are being released claiming to be 'open'. However, the definition of open model remains widely debated. In this article, we adapt a recently proposed evidence-based framework for assessing openness in LLMs to the music domain. Based on feedback gathered through a survey of 110 participants from the Music Information Retrieval (MIR) community, we refine the framework into MusGO (Music-Generative Open AI), which comprises 13 openness categories, classified into essential (8) and nice-to-have (5). We evaluate more than a dozen state-of-the-art generative models and provide an openness leaderboard that is fully open to public scrutiny and community contribution. Through this work, we aim to clarify the concept of openness in music-generative models and promote their transparent and responsible development.