Explainable AI has the potential to support more interactive and fluid co-creative AI systems which can creatively collaborate with people. To do this, creative AI models need to be amenable to debugging by offering eXplainable AI (XAI) features which are inspectable, understandable, and modifiable. However, currently there is very little XAI for the arts. In this work, we demonstrate how a latent variable model for music generation can be made more explainable; specifically we extend MeasureVAE which generates measures of music. We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions. We suggest that in doing so we bridge the gap between the latent space and the generated musical outcomes in a meaningful way which makes the model and its outputs more explainable and more debuggable.

翻译：可解释人工智能有望支持更具交互性与流畅性的共创式AI系统，使其能够与人类进行创造性协作。为此，创意AI模型需具备可调试性，通过提供可检查、可理解且可修改的可解释AI（XAI）功能。然而，目前面向艺术领域的XAI研究极其有限。本研究展示如何提升音乐生成隐变量模型的可解释性：我们具体扩展了用于生成音乐小节的MeasureVAE模型。通过以下方式增强模型可解释性：i）利用隐空间正则化强制隐空间中特定维度映射至有意义的音乐属性；ii）构建用户界面反馈循环，允许用户调整隐空间维度并实时观察调整结果；iii）提供隐空间中音乐属性的可视化表征，帮助用户理解并预测隐空间维度变化的效果。我们认为，通过上述方法，我们以有意义的方式弥合了隐空间与生成音乐结果之间的鸿沟，使模型及其输出更易于解释与调试。