  The ability to explain decisions to its end-users is a necessity to deploy AI
as critical decision support. Yet making AI explainable to end-users is a
relatively ignored and challenging problem. To bridge the gap, we first
identified twelve end-user-friendly explanatory forms that do not require
technical knowledge to comprehend, including feature-, example-, and rule-based
explanations. We then instantiated the explanatory forms as prototyping cards
in four AI-assisted critical decision-making tasks, and conducted a user study
to co-design low-fidelity prototypes with 32 layperson participants. The
results verified the relevance of using the explanatory forms as building
blocks of explanations, and identified their proprieties (pros, cons,
applicable explainability needs, and design implications). The explanatory
forms, their proprieties, and prototyping support constitute the
End-User-Centered explainable AI framework EUCA. It serves as a practical
prototyping toolkit for HCI/AI practitioners and researchers to build
end-user-centered explainable AI.
  The EUCA framework is available at http://weina.me/end-user-xai
