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：非技术终端用户是当前最先进的可解释人工智能（XAI）技术中沉默且不可见的用户。他们对AI可解释性的需求和要求未被纳入XAI技术的设计与评估，而这些技术本应用于向终端用户解释AI决策的原理并协助其关键决策。这使得XAI技术在医疗、刑事司法、金融和自动驾驶系统等高风险评估应用中效果不佳甚至有害。为系统性地理解终端用户需求以支持XAI技术开发，我们开展了EUCA用户研究，邀请32名非专业参与者在四项AI辅助关键任务中进行测试。该研究识别出基于特征、实例和规则的XAI技术（通过终端用户友好的解释形式体现）及XAI评估目标（通过解释目标体现）的全面用户需求，这些需求被证明可直接激发新型XAI算法及评估指标的提出。EUCA研究结果、用于技术规范的已识别解释形式与目标，以及EUCA研究数据集，将支持以终端用户为中心的XAI技术设计与评估，促进可访问、安全且负责任的AI发展。