人类对可解释性的评估：人工智能生成的音乐知识的案例
Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge
摘要：
机器学习模型的可解释性越来越受到人工智能（AI）和人机交互（HCI）领域研究人员的关注。大多数现有的工作集中在决策，而我们认为知识发现。特别是，我们专注于评估人工智能在艺术和人文领域发现的知识/规则。从一个特定的场景，我们提出了一个实验程序来收集和评估人工智能生成的音乐理论/规则呈现为复杂的符号/数字对象的人类生成的口头解释。我们的目标是揭示从AI来源解码表达信息的过程中的可能性和挑战。我们将此视为迈向以下目标的第一步：1）更好地设计人类可解释的AI表示; 2）评估AI发现的知识表示的可解释性的通用方法。
英文摘要：
Interpretability of machine learning models has gained more and more attention among researchers in the artificial intelligence (AI) and human-computer interaction (HCI) communities. Most existing work focuses on decision making, whereas we consider knowledge discovery. In particular, we focus on evaluating AI-discovered knowledge/rules in the arts and humanities. From a specific scenario, we present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to reveal both the possibilities and the challenges in such a process of decoding expressive messages from AI sources. We treat this as a first step towards 1) better design of AI representations that are human interpretable and 2) a general methodology to evaluate interpretability of AI-discovered knowledge representations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
