Liao
A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on the topic. While many rec- ognize the necessity to incorporate explainability features in AI systems, how to address real-world user needs for under- standing AI remains an open question. By interviewing 20 UX and design practitioners working on various AI products, we seek to identify gaps between the current XAI algorithmic work and practices to create explainable AI products. To do so, we develop an algorithm-informed XAI question bank in which user needs for explainability are represented as proto- typical questions users might ask about the AI, and use it as a study probe. Our work contributes insights into the design space of XAI, informs efforts to support design practices in this space, and identifies opportunities for future XAI work. We also provide an extended XAI question bank and discuss how it can be used for creating user-centered XAI. Author Keywords Explainable AI; human-AI interaction; User experience