This study investigates the role of robot bowing in trust repair, focusing on how human-like movement impacts trust in human-robot interaction. We manipulated the movement quality across four conditions: human-like, constant-speed, abrupt, and no-movement. Specifically for the human-like movement, we analyzed the Japanese hospitality (called “omotenashi”) gesture, which expresses attentiveness and respect to others and was translated into robot bowing movement. The experimental results indicated that human-like movement did not significantly affect trust repair, while constant-speed and abrupt movements showed improvements in subjective trust. The study highlighted the need for movement designs with considerations of the robot's appearance to facilitate effective trust repair.

Explainable AI (XAI) has been developed to make AI understandable to humans by providing explanations of its outputs. However, multiple types of AI explanations displayed on a screen could distort users’ trust in AI and their decision to rely on it. This could lead to poor task performance. In this study, we experimentally investigated the influence of AI explanations on trust and acceptance of AI results using a visual task. As a result, we found that participants increased their trust and acceptance of AI results with multiple types of explanations even though this did not improve task performance. These results, showing over-trust and over-reliance in human-agent interaction, were discussed along with cognitive load and cognitive bias caused by XAI.

This study investigated how a robot head motion towards an AI attention heatmap during a visual identification task influences a human user’s trust in eXplainable AI (XAI). The findings revealed that the robot head motion presented in a video increased the user’s acceptance of AI-generated results compared to the robot eye gaze displayed in a static image with or without the AI attention heatmap. However, displaying the heatmap improved task performance more than displaying no heatmap with or without the robot. Overall, these results suggest a possibility that showing a robot head motion towards an AI attention heatmap in a movie can serve as an interpretable XAI for visual tasks.

This study investigated how displaying an AI attention heatmap influences human acceptance of the AI’s suggestions in accordance with the interpretability of the heatmap. We conducted an experiment using a visual task where the participants were required to decide whether to accept or reject an AI’s suggestions. The participants could see the suggestions with an AI attention heatmap, the heatmap with the AI pointing to it (displayed as a laser dot cursor), the heatmap with a robot pointing (a robot using a stick to point to the AI heatmap displayed on a tablet), or no heatmap. The experimental results revealed that human acceptance of AI suggestions differed depending on the interpretability of the heatmap, especially when the heatmap was displayed with AI pointing. Also, additional analysis revealed an effect on acceptance due to the AI pointing to the heatmap that was found only in a high-task difficulty situation. An AI pointing to its attention heatmap is considered to trigger people to reason about particular AI processes and accept its suggestions. This study showed that an AI pointing to its attention heatmap could be used to control human behaviors in human-agent interaction.

This study investigated how displaying a robot’s attention heatmap while the robot pointing gesture at it influences human trust and acceptance of its outputs. We conducted an experiment using two types of visual tasks. In these tasks, the participants were required to decide whether to accept or reject the answers of an AI or robot. The participants could see the answers with an AI attention heatmap, the heatmap with AI pointing (displayed as a laser dot cursor), a robot attention heatmap with robot pointing (pointing at a certain location on the heatmap displayed on a tablet with a stick), or no heatmap. The experimental results revealed that the AI and robot pointing at their attention heatmaps lowered the participants’ acceptance of their answers when the heatmaps had low interpretability in a more difficult task. Also, the robot pointing at the heatmaps showed the possibility of increasing acceptance of its answer when the heatmaps had high interpretability in a more difficult task. In addition, the acceptance of the robot’s answers correlated with emotional trust in the robot. This study demonstrates that a robot pointing gesture at its attention heatmap could be used to control human behaviors and emotional trust in human-robot interactions.

This study investigated whether human trust in a social robot with anthropomorphic physicality is similar to that in an AI agent or in a human in order to clarify how anthropomorphic physicality influences human trust in an agent. We conducted an online experiment using two types of cognitive tasks, calculation and emotion recognition tasks, where participants answered after referring to the answers of an AI agent, a human, or a social robot. During the experiment, the participants rated their trust levels in their partners. As a result, trust in the social robot was basically neither similar to that in the AI agent nor in the human and instead settled between them. The results showed a possibility that manipulating anthropomorphic features would help assist human users in appropriately calibrating trust in an agent.

In this study, the nature of human trust in communication robots was experimentally investigated comparing with trusts in other people and artificial intelligence (AI) systems. The results of the experiment showed that trust in robots is basically similar to that in AI systems in a calculation task where a single solution can be obtained and is partly similar to that in other people in an emotion recognition task where multiple interpretations can be acceptable. This study will contribute to designing a smooth interaction between people and communication robots.