A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques to reframe the technical space of XAI, also serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.

翻译：AI系统的一个普遍设计问题是,如何提供恰当的信息,帮助用户理解AI。可以解释的AI(XAI)技术领域产生了丰富的技术工具箱。设计者现在的任务是如何选择最合适的XAI技术并将其转化为UX解决方案。我们以前研究XAI UX周围的设计挑战时,我们从这项工作中了解到,这项工作提出了应对这些挑战的设计过程。我们审查了我们和相关的先前工作,以确定程序应满足的要求,并据此提出一个问题驱动设计程序,为用户的需要、对XAI技术的选择、设计和对XAIUX的所有用户问题进行评估提供依据。我们为原型用户问题和XAI技术的外形设计者提供了制图指南,以重新界定XAI的技术空间,同时也作为支持设计者和AI工程师之间合作的边界物体。我们用设计XAI用于健康不良事件的预测,并讨论处理AI系统的设计挑战方面的经验教训,以此来证明这一点。