标题： 有意义的模型：利用概念结构提高机器学习可解释性 显示英文标题

标题： Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability

摘要： 过去十年见证了先进机器学习模型的飞速发展；然而，除非人类用户能够理解这些模型，否则它们毫无用处。 在这里，我们展示了如何利用心智构建概念和意义的方式来创建更可解释的机器学习模型。 通过提出一种以“形式”和“功能”来分类概念的新方法，我们阐明了意义的本质，并提出了提高模型可理解性的建议。 随着机器学习开始渗透日常生活，可解释的模型可能成为领域专家作者与非专家用户之间的桥梁。 显示英文摘要

摘要： The last decade has seen huge progress in the development of advanced machine learning models; however, those models are powerless unless human users can interpret them. Here we show how the mind's construction of concepts and meaning can be used to create more interpretable machine learning models. By proposing a novel method of classifying concepts, in terms of 'form' and 'function', we elucidate the nature of meaning and offer proposals to improve model understandability. As machine learning begins to permeate daily life, interpretable models may serve as a bridge between domain-expert authors and non-expert users.