A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation. However, such systems are notorious for their complexity and opaqueness making quality documentation a non-trivial task. Furthermore, little is known about what makes such documentation "good." In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short. Then, using those dimensions, we evaluate three different approaches for eliciting intelligent system documentation. We show how the dimensions identify shortcomings in such documentation and posit how such dimensions can be use to further enable users to provide documentation that is suitable to a given persona or use case.

中文翻译：

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为智能系统评估和获取高质量文档

建立在机器学习和人工智能基础上的智能系统中信任和透明度的一个重要组成部分是开发清晰易懂的文档。然而，此类系统因其复杂性和不透明性而臭名昭著，这使得质量文档成为一项重要任务。此外，人们对是什么使此类文档“好”知之甚少。在本文中，我们提出并评估了一组质量维度，以确定此类文档在哪些方面存在不足。然后，使用这些维度，我们评估了用于引出智能系统文档的三种不同方法。我们展示了维度如何识别此类文档中的缺点，并假设如何使用此类维度来进一步使用户能够提供适合给定角色或用例的文档。