  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.
