Reducing Barriers to the Use of Marginalised Music Genres in AI

TL;DR

The paper addresses barriers to using marginalised music genres in AI-generated music, arising from reliance on large datasets that bias models toward dominant genres. It reports a four-month international project combining expert interviews and a UK–China hybrid workshop to investigate XAI, HCAI, and RAIs approaches for small-data music generation, focusing on ethics, bias, transparency, and usability for musicians. Through thematic analysis, it identifies four themes—access barriers, stakeholder dynamics, technology innovation with small-dataset strategies, and sustainable development—advocating concrete interventions such as novel dataset construction, training explanations, and an online repository with XAI mappings to enable cross-genre collaboration. The work argues for no single model capable of handling all genres, emphasizes cultural representation and bias reduction, and outlines plans to build an International Responsible AI Music community to advance inclusive, explainable, and responsible AI-assisted music across marginalised traditions.

Abstract

AI systems for high quality music generation typically rely on extremely large musical datasets to train the AI models. This creates barriers to generating music beyond the genres represented in dominant datasets such as Western Classical music or pop music. We undertook a 4 month international research project summarised in this paper to explore the eXplainable AI (XAI) challenges and opportunities associated with reducing barriers to using marginalised genres of music with AI models. XAI opportunities identified included topics of improving transparency and control of AI models, explaining the ethics and bias of AI models, fine tuning large models with small datasets to reduce bias, and explaining style-transfer opportunities with AI models. Participants in the research emphasised that whilst it is hard to work with small datasets such as marginalised music and AI, such approaches strengthen cultural representation of underrepresented cultures and contribute to addressing issues of bias of deep learning models. We are now building on this project to bring together a global International Responsible AI Music community and invite people to join our network.