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Explainable AI has the potential to support more interactive and fluidco-creative AI systems which can creatively collaborate with people. To dothis, creative AI models need to be amenable to debugging by offeringeXplainable AI (XAI) features which are inspectable, understandable, andmodifiable. However, currently there is very little XAI for the arts. In thiswork, we demonstrate how a latent variable model for music generation can bemade more explainable; specifically we extend MeasureVAE which generatesmeasures of music. We increase the explainability of the model by: i) usinglatent space regularisation to force some specific dimensions of the latentspace to map to meaningful musical attributes, ii) providing a user interfacefeedback loop to allow people to adjust dimensions of the latent space andobserve the results of these changes in real-time, iii) providing avisualisation of the musical attributes in the latent space to help peopleunderstand and predict the effect of changes to latent space dimensions. Wesuggest that in doing so we bridge the gap between the latent space and thegenerated musical outcomes in a meaningful way which makes the model and itsoutputs more explainable and more debuggable.

Exploring XAI for the Arts: Explaining Latent Space in Generative Music