论文标题
旨在评估和诱导智能系统的高质量文档
Towards evaluating and eliciting high-quality documentation for intelligent systems
论文作者
论文摘要
建立在机器学习和人工智能上的智能系统中信任和透明度的重要组成部分是清晰，可理解的文档的发展。但是，这种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非平凡的任务。此外，对这种文档“好”的原因知之甚少。在本文中，我们提出并评估一组质量维度，以确定这种类型的文档的缺点。然后，使用这些维度，我们评估了三种不同的方法来启发智能系统文档。我们展示了如何确定此类文档中的缺点，并提出了如何使用此类维度以进一步使用户提供适合给定角色或用例的文档。
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.
