Reducing Barriers to the Use of Marginalised Music Genres in AI

TL;DR

This paper explores how explainable AI can reduce barriers to using marginalized music genres in AI music generation, emphasizing transparency, bias reduction, and cultural representation.

Contribution

It presents a four-month international study on XAI challenges and opportunities for integrating marginalized music genres into AI systems.

Findings

Improving transparency and control in AI models for marginalized music genres

Fine-tuning large models with small datasets reduces bias

Enhancing cultural representation and addressing bias issues

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.