The rapidly evolving field of Explainable Artificial Intelligence (XAI) has generated significant interest in developing methods to make AI systems more transparent and understandable. However, the problem of explainability cannot be exhaustively solved in the abstract, as there is no single approach that can be universally applied to generate adequate explanations for any given AI system, and this is especially true in the arts. In this position paper, we propose an Explanatory Pragmatism (EP) framework for XAI in music performance, emphasising the importance of context and audience in the development of explainability requirements. By tailoring explanations to specific audiences and continuously refining them based on feedback, EP offers a promising direction for enhancing the transparency and interpretability of AI systems in broad artistic applications and more specifically to music performance.

翻译：快速发展的可解释人工智能（XAI）领域已引发对开发使人工智能系统更透明、更易理解方法的广泛关注。然而，可解释性问题无法在抽象层面得到彻底解决，因为不存在能普遍适用于任何AI系统以生成充分解释的单一方法，尤其是在艺术领域。在这篇立场论文中，我们提出了一种面向音乐表演中XAI的解释实用主义（EP）框架，强调在可解释性需求开发中上下文与受众的重要性。通过针对特定受众定制解释，并基于反馈持续优化，EP为提升AI系统在广泛艺术应用（尤其是音乐表演）中的透明度与可解释性提供了有前景的方向。