  The ability to explain decisions to end-users is a necessity to deploy AI as
critical decision support. Yet making AI explainable to non-technical end-users
is a relatively ignored and challenging problem. To bridge the gap, we first
identify twelve end-user-friendly explanatory forms that do not require
technical knowledge to comprehend, including feature-, example-, and rule-based
explanations. We then instantiate the explanatory forms as prototyping cards in
four AI-assisted critical decision-making tasks, and conduct a user study to
co-design low-fidelity prototypes with 32 layperson participants. The results
confirm the relevance of using explanatory forms as building blocks of
explanations, and identify their proprieties - pros, cons, applicable
explanation goals, and design implications. The explanatory forms, their
proprieties, and prototyping supports (including a suggested prototyping
process, design templates and exemplars, and associated algorithms to actualize
explanatory forms) constitute the End-User-Centered explainable AI framework
EUCA, and is available at http://weinajin.github.io/end-user-xai . It serves as
a practical prototyping toolkit for HCI/AI practitioners and researchers to
understand user requirements and build end-user-centered explainable AI.
