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The rapidly evolving field of Explainable Artificial Intelligence (XAI) hasgenerated significant interest in developing methods to make AI systems moretransparent and understandable. However, the problem of explainability cannotbe exhaustively solved in the abstract, as there is no single approach that canbe universally applied to generate adequate explanations for any given AIsystem, and this is especially true in the arts. In this position paper, wepropose an Explanatory Pragmatism (EP) framework for XAI in music performance,emphasising the importance of context and audience in the development ofexplainability requirements. By tailoring explanations to specific audiencesand continuously refining them based on feedback, EP offers a promisingdirection for enhancing the transparency and interpretability of AI systems inbroad artistic applications and more specifically to music performance.

A Context-Sensitive Approach to XAI in Music Performance