What you need to know about a learning robot: Identifying the enabling architecture of complex systems

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.

Adding Why to What? Analyses of an Everyday Explanation

In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.