Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context

Domain experts utilize a decision-support system depending on an artificial intelligence (AI) algorithm. Likewise, researchers in brain-computer interface (BCI) have recently employed deep learning (DL) algorithms for decoding and analyzing neural signals. Despite its outstanding performance, the BCI technology with the DLs has pointed out that it has a potential problem of low transparency due to algorithmic complexity of the models. On this problem, explainable artificial intelligence (XAI) can be a solution to make an AI algorithm and its decisions more interpretable. However, the explanation from the XAI has been emphasized that it should be designed corresponding with the user's different expectations which are contextually variable. Thus, our study aims to propose an explanation interface for the BCI expert under Pragmatism structuralizing an explanation with scientific knowledge in a contrastive manner. For this work, we conduct a contextual design process with five BCI experts, specifically conducting a contextual inquiry and work modeling to extract design requirements from their expertise in their work environment; next, designing and evaluating an interactive prototype of the explanation interface. The results indicated that our prototype has the advantages of increasing contextual understanding and intuitive interface design. Yet, there were also challenges on the explanation for novice users without prior knowledge on the XAI and objective understanding of the AI model with enough interpretability. This study contributes to providing a theoretical framework based on Pragmatism and designing a user-centered XAI system for domain experts in a specific context.