  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, 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.
