Carsten Schulte

Lutz Terfloth, Michael Schaffer, Heike M. Buhl et al.

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.

Lutz Terfloth, Heike M. Buhl, Vivien Lohmer et al.

Purpose: Understanding a technical artifact requires grasping both its internal structure (Architecture) and its purpose and significance (Relevance), as formalized by Dual Nature Theory. This controlled experimental study investigates whether how explainers address these perspectives affects explainees' understanding. Methods: In a between-subjects experiment, 104 participants received explanations of the board game Quarto! from trained confederates in one of three conditions: Architecture-focused (A), Relevance-focused (R), or Integrated (AR). Understanding was assessed on comprehension (knowing that) and enabledness (knowing how). Results: The A and R conditions produced equivalent understanding despite different explanation content. The AR condition yielded significantly higher enabledness than the focused conditions combined $\mathrm{F}(1, 102) = 4.83$, $p = .030$, $η^2_p = .045$}, while no differences emerged for comprehension. Conclusion: Integrating Architecture and Relevance specifically enhances explainees' ability to apply their understanding in practice, suggesting that fostering agency with technical artifacts requires bridging both perspectives. This has implications for technology education and explainable AI design.

Michael Erol Schaffer, Lutz Terfloth, Carsten Schulte et al.

In explanations, explainers have mental representations of explainees' developing knowledge and shifting interests regarding the explanandum. These mental representations are dynamic in nature and develop over time, thereby enabling explainers to react to explainees' needs by adapting and customizing the explanation. XAI should be able to react to explainees' needs in a similar manner. Therefore, a component that incorporates aspects of explainers' mental representations of explainees is required. In this study, we took first steps by investigating explainers' mental representations in everyday explanations of technological artifacts. According to the dual nature theory, technological artifacts require explanations with two distinct perspectives, namely observable and measurable features addressing "Architecture" or interpretable aspects addressing "Relevance". We conducted extended semi structured pre-, post- and video recall-interviews with explainers (N=9) in the context of an explanation. The transcribed interviews were analyzed utilizing qualitative content analysis. The explainers' answers regarding the explainees' knowledge and interests with regard to the technological artifact emphasized the vagueness of early assumptions of explainers toward strong beliefs in the course of explanations. The assumed knowledge of explainees in the beginning is centered around Architecture and develops toward knowledge with regard to both Architecture and Relevance. In contrast, explainers assumed higher interests in Relevance in the beginning to interests regarding both Architecture and Relevance in the further course of explanations. Further, explainers often finished the explanation despite their perception that explainees still had gaps in knowledge. These findings are transferred into practical implications relevant for user models for adaptive explainable systems.