Nowadays, we are dealing more and more with robots and AI in everyday life. However, their behavior is not always apparent to most lay users, especially in error situations. As a result, there can be misconceptions about the behavior of the technologies in use. This, in turn, can lead to misuse and rejection by users. Explanation, for example, through transparency, can address these misconceptions. However, it would be confusing and overwhelming for users if the entire software or hardware was explained. Therefore, this paper looks at the 'enabling' architecture. It describes those aspects of a robotic system that might need to be explained to enable someone to use the technology effectively. Furthermore, this paper is concerned with the 'explanandum', which is the corresponding misunderstanding or missing concepts of the enabling architecture that needs to be clarified. We have thus developed and present an approach for determining this 'enabling' architecture and the resulting 'explanandum' of complex technologies.

翻译：如今，我们在日常生活中越来越多地与机器人和人工智能打交道。然而，对于大多数非专业用户而言，其行为并非总是一目了然，尤其是在错误情境下。这可能导致用户对所使用技术的行为产生误解，进而引发误用和用户抵触。通过透明化等方式进行解释可以消除这些误解。然而，如果解释整个软件或硬件系统，用户会感到困惑和负担过重。因此，本文聚焦于“使能”架构，描述机器人系统中可能需要解释的方面，以帮助用户有效使用该技术。此外，本文还关注“被解释项”，即需要澄清的、关于使能架构的相应误解或缺失概念。我们据此开发并展示了一种方法，用于确定复杂技术的此类“使能”架构及由此产生的“被解释项”。