The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs.

翻译：随着人工智能在航空交通管制等安全关键领域的日益普及，催生出既实用高效、又具备一定可解释性以获取人类信任与接受的系统。本结构化文献分析考察了236篇关于人工智能可解释性与接受度要求的文章。研究结果包括对48篇文章的全面综述，内容涵盖：人们认为人工智能具有可解释性所需的信息、接受人工智能所需的信息，以及促进对人工智能信任的表征与交互方法。结果表明，两大主要用户群体为：需要了解模型内部运作信息的开发者，以及需要了解人工智能结果或行为信息的最终用户。用户的信息需求在具体性、复杂性和紧迫性方面存在差异，且必须考虑具体情境、领域知识及用户的认知资源。对人工智能系统的接受度取决于系统功能与性能信息、隐私与伦理考量、针对个性化偏好的目标支持信息，以及建立系统信任度的信息。关于系统局限性和潜在失败的信息可提升接受度与信任度。受信赖的交互方法应具有类人特性，包括自然语言、语音、文本及图表、动画等可视化表征。我们的研究结果对未来开发以人为中心的人工智能系统具有重要启示意义，因此可作为进一步开展特定场景用户需求研究的输入依据。