Non-technical end-users are silent and invisible users of the state-of-the-art explainable artificial intelligence (XAI) technologies. Their demands and requirements for AI explainability are not incorporated into the design and evaluation of XAI techniques, which are developed to explain the rationales of AI decisions to end-users and assist their critical decisions. This makes XAI techniques ineffective or even harmful in high-stakes applications, such as healthcare, criminal justice, finance, and autonomous driving systems. To systematically understand end-users' requirements to support the technical development of XAI, we conducted the EUCA user study with 32 layperson participants in four AI-assisted critical tasks. The study identified comprehensive user requirements for feature-, example-, and rule-based XAI techniques (manifested by the end-user-friendly explanation forms) and XAI evaluation objectives (manifested by the explanation goals), which were shown to be helpful to directly inspire the proposal of new XAI algorithms and evaluation metrics. The EUCA study findings, the identified explanation forms and goals for technical specification, and the EUCA study dataset support the design and evaluation of end-user-centered XAI techniques for accessible, safe, and accountable AI.

翻译：非技术背景的终端用户是当前最先进的可解释人工智能（XAI）技术中沉默而隐形的使用者。他们对AI可解释性的需求与要求未被纳入XAI技术的设计与评估中——而XAI技术本是为了向终端用户解释AI决策逻辑并辅助其关键决策而开发的。这导致在医疗、刑事司法、金融、自动驾驶系统等高利害应用中，XAI技术效率低下甚至可能造成危害。为系统性地理解终端用户需求以支撑XAI技术开发，我们通过EUCA用户研究，在四项AI辅助关键任务中征集了32名普通人参与者。该研究系统识别出基于特征、示例和规则的XAI技术（通过终端用户友好的解释形式呈现）及XAI评估目标（通过解释目标呈现）的完整用户需求，这些成果可直接启发新型XAI算法与评估指标的提出。EUCA研究结论、为技术规范而识别的解释形式与目标，以及EUCA研究数据集，共同支撑面向终端用户、实现可访问、安全且可问责AI的XAI技术设计与评估。