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A surge of interest in explainable AI (XAI) has led to a vast collection ofalgorithmic work on the topic. While many recognize the necessity toincorporate explainability features in AI systems, how to address real-worlduser needs for understanding AI remains an open question. By interviewing 20 UXand design practitioners working on various AI products, we seek to identifygaps between the current XAI algorithmic work and practices to createexplainable AI products. To do so, we develop an algorithm-informed XAIquestion bank in which user needs for explainability are represented asprototypical questions users might ask about the AI, and use it as a studyprobe. Our work contributes insights into the design space of XAI, informsefforts to support design practices in this space, and identifies opportunitiesfor future XAI work. We also provide an extended XAI question bank and discusshow it can be used for creating user-centered XAI.

Questioning the AI: Informing Design Practices for Explainable AI User Experiences