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Recent years have seen a surge of interest in the field of explainable AI(XAI), with a plethora of algorithms proposed in the literature. However, alack of consensus on how to evaluate XAI hinders the advancement of the field.We highlight that XAI is not a monolithic set of technologies -- researchersand practitioners have begun to leverage XAI algorithms to build XAI systemsthat serve different usage contexts, such as model debugging anddecision-support. Algorithmic research of XAI, however, often does not accountfor these diverse downstream usage contexts, resulting in limited effectivenessor even unintended consequences for actual users, as well as difficulties forpractitioners to make technical choices. We argue that one way to close the gapis to develop evaluation methods that account for different user requirementsin these usage contexts. Towards this goal, we introduce a perspective ofcontextualized XAI evaluation by considering the relative importance of XAIevaluation criteria for prototypical usage contexts of XAI. To explore thecontext dependency of XAI evaluation criteria, we conduct two survey studies,one with XAI topical experts and another with crowd workers. Our results urgefor responsible AI research with usage-informed evaluation practices, andprovide a nuanced understanding of user requirements for XAI in different usagecontexts.

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI