With advancements in artificial intelligence (AI), explainable AI (XAI) has emerged as a promising tool for enhancing the explainability of complex machine learning models. However, the explanations generated by an XAI may lead to cognitive biases among human users. To address this problem, this study aims to investigate how to mitigate users’ cognitive biases based on their individual characteristics. In the literature review, we found two factors that can be helpful in remedying biases: 1) debiasing strategies that have been reported to potentially reduce biases in users’ decision-making via additional information or change in information delivery, and 2) explanation modality types. To examine these factors’ effects, we conducted an experiment with a 4 (debiasing strategy) × 3 (explanation type) between-subject design. In the experiment, participants were exposed to an explainable interface that provides an AI’s outcomes with explanatory information, and their behavioral and attitudinal responses were collected. Specifically, we statistically examined the effects of textual and visual explanations on users’ trust and confirmation bias toward AI systems, considering the moderating effects of debiasing methods and watching time. The results demonstrated that textual explanations lead to higher trust in XAI systems compared to visual explanations. Moreover, we found that textual explanations are particularly beneficial for quick decision-makers to evaluate the outputs of AI systems. Next, the results indicated that the cognitive bias can be effectively mitigated by providing users with a priori information. These findings have theoretical and practical implications for designing AI-based decision support systems that can generate more trustworthy and equitable explanations.

Majority opinions are often observed in online environments. Previous studies have demonstrated that majority opinions are constructed because people with minority opinions have fear of isolation, which forces them to be silent. However, we often observe that online users with different minority opinions fight each other, even though there are only a few. To explain this phenomenon, we developed a new theoretical model and examined it through the analysis of Reddit data. The results show that users in small communities expressed relatively homogeneous and less negative opinions, and constructed majority opinions that were not as strong as those in large communities. Contrarily, users in large communities showed relatively heterogeneous and more negative opinions, and built majority opinions more strongly than those in small communities. This implies that the model can properly explain why and how majority-opinion users are silent and why minority-opinion users are loud.

Contrary to the traditional concept of artificial intelligence, explainable artificial intelligence (XAI) aims to provide explanations for the prediction results and make users perceive the system as being reliable. However, despite its importance, only a few studies have investigated how the explanations of an XAI system should be designed. This study investigates how people attribute the perceived ability of XAI systems based on perceived attributional qualities and how the power status of the XAI and anthropomorphism affect the attribution process. In a laboratory experiment, participants (N = 500) read a scenarios of using an XAI system with either lower or higher power status and reported their perceptions of the system. Results indicated that an XAI system with a higher power status caused users to perceive the outputs of the XAI system to be more controllable by intention, and higher perceived stability and uncontrollability resulted in greater confidence in the system’s ability. The effect of perceived controllability on perceived ability was moderated by the extent to which participants anthropomorphised the system. Several design implications for XAI systems are suggested based on our findings.

For user experience studies, the affordance theory has been used to describe a user’s intuitive perception and action. This theory, however, has often faced problems in application due to the different viewpoints of the ecological psychology and other fields of application study. To address this issue, we adopt two strategies in this study. First, we review the existing explications of the affordance theory in ecological psychology and rectify issues that have hindered the use of concepts in the affordance theory for user experience analysis. In addressing these issues, we suggest revised formal expressions and propose a new typological system. Second, by organizing the revised formal expressions and the new typology of the affordance into a heterophenomenological frame, we suggest a research framework for user experience analysis. We present an application example of the framework for demonstration purposes. We expect that the suggested framework will enable better descriptions of various phenomena occurring in the physical, social, and self dimensions and designs of products and services.

This study proposes a new approach to developing a user behavior model to explain how a user finds the optimal use. This is achieved by considering user concerns, task significances, affordances, and emotional responses as the interaction components and by exploring behavior sequences for a goal in using a product the first time. The tasks in the same group at each level in the user concern structure are therefore in a competing relationship in going up to a higher task. The task tree with the significances and the affordance probabilities can be analyzed. The order of a user’s exploring behavior sequences can be determined through comparisons of the expected significances, which can be obtained by the modified subjective expected utility theory. A user’s emotional responses for the tasks that a behavior sequence is composed of can be calculated by the modified decision affect theory. Here, the emotional response refers to a user’s internal reactions for the degree to which a product’s affordance features can meet his or her mental model in use. The average emotional response for a behavior sequence can be a user’s decisional factor for the optimal use method in using a product with a goal. Also, the design problems of a product can be checked from users’ point of view, and the emotional losses/changes by usage failures can be discussed. For an illustrative purpose, the proposed model is applied to a numerical example with some assumptions.