Explanations in XAI are typically developed by AI experts and focus on algorithmic transparency and the inner workings of AI systems. Research has shown that such explanations do not meet the needs of users who do not have AI expertise. As a result, explanations are often ineffective in making system decisions interpretable and understandable. We aim to strengthen a socio-technical view of AI by following a Human-Centered Explainable Artificial Intelligence (HC-XAI) approach, which investigates the explanation needs of end-users (i.e., subject matter experts and lay users) in specific usage contexts. One of the most influential works in this area is the XAI Question Bank (XAIQB) by Liao et al. The authors propose a set of questions that end-users might ask when using an AI system, which in turn is intended to help developers and designers identify and address explanation needs. Although the XAIQB is widely referenced, there are few reports of its use in practice. In particular, it is unclear to what extent the XAIQB sufficiently captures the explanation needs of end-users and what potential problems exist in the practical application of the XAIQB. To explore these open questions, we used the XAIQB as the basis for analyzing 12 think-aloud software explorations with subject matter experts. We investigated the suitability of the XAIQB as a tool for identifying explanation needs in a specific usage context. Our analysis revealed a number of explanation needs that were missing from the question bank, but that emerged repeatedly as our study participants interacted with an AI system. We also found that some of the XAIQB questions were difficult to distinguish and required interpretation during use. Our contribution is an extension of the XAIQB with 11 new questions. In addition, we have expanded the descriptions of all new and existing questions to facilitate their use.

翻译：XAI中的解释通常由AI专家开发，侧重于算法透明度和AI系统的内部工作机制。研究表明，此类解释无法满足非AI专家用户的需求。因此，解释往往难以有效使系统决策具备可解释性和可理解性。我们旨在通过采用以人为本的可解释人工智能（HC-XAI）方法，强化AI的社会技术视角，该方法调查最终用户（即领域专家和普通用户）在特定使用情境中的解释需求。该领域最具影响力的工作之一是由Liao等人提出的XAI问题库（XAIQB）。作者提出了一组最终用户在使用AI系统时可能提出的问题，旨在帮助开发者和设计者识别并应对解释需求。尽管XAIQB被广泛引用，但关于其实际应用的报告寥寥无几。尤其不清楚的是，XAIQB在多大程度上充分覆盖了最终用户的解释需求，以及XAIQB在实际应用中存在哪些潜在问题。为探讨这些悬而未决的问题，我们以XAIQB为基础，分析了12次与领域专家进行的出声思维软件探索实验。我们研究了XAIQB作为工具在特定使用情境中识别解释需求的适用性。分析发现，问题库缺失若干解释需求，而这些需求在研究参与者与AI系统交互过程中反复出现。我们还发现部分XAIQB问题难以区分，在使用中需要解释。我们的贡献是为XAIQB新增了11个问题。此外，我们扩展了所有新问题和原有问题的描述，以促进其应用。