Facilitation of human empathy through self-disclosure of anthropomorphic agents

As AI technologies progress, social acceptance of AI agents including intelligent virtual agents and robots is getting to be even more important for more applications of AI in human society. One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. By empathizing, humans take positive and kind actions toward agents, and emphasizing makes it easier for humans to accept agents. In this study, we focused on self-disclosure from agents to humans in order to realize anthropomorphic agents that elicit empathy from humans. Then, we experimentally investigated the possibility that an agent's self-disclosure facilitates human empathy. We formulate hypotheses and experimentally analyze and discuss the conditions in which humans have more empathy for agents. This experiment was conducted with a three-way mixed plan, and the factors were the agents' appearance (human, robot), self-disclosure (high-relevance self-disclosure, low-relevance self-disclosure, no self-disclosure), and empathy before and after a video stimulus. An analysis of variance was performed using data from 576 participants. As a result, we found that the appearance factor did not have a main effect, and self-disclosure, which is highly relevant to the scenario used, facilitated more human empathy with statistically significant difference. We also found that no self-disclosure suppressed empathy. These results support our hypotheses.Comment: 20 pages, 8 figures, 2 tables, submitted to PLOS ONE Journa

Exploring Human Teachers' Interpretations of Trainee Robots' Nonverbal Behaviour and Errors

In the near future, socially intelligent robots that can learn new tasks from humans may become widely available and gain an opportunity to help people more and more. In order to successfully play a role, not only should intelligent robots be able to interact effectively with humans while they are being taught, but also humans should have the assurance to trust these robots after teaching them how to perform tasks. When human students learn, they usually provide nonverbal cues to display their understanding of and interest in the material. For example, they sometimes nod, make eye contact or show meaningful facial expressions. Likewise, a humanoid robot's nonverbal social cues may enhance the learning process, in case the provided cues are legible for human teachers. To inform designing such nonverbal interaction techniques for intelligent robots, our first work investigates humans' interpretations of nonverbal cues provided by a trainee robot. Through an online experiment (with 167 participants), we examine how different gaze patterns and arm movements with various speeds and different kinds of pauses, displayed by a student robot when practising a physical task, impact teachers' understandings of the robot’s attributes. We show that a robot can appear differently in terms of its confidence, proficiency, eagerness to learn, etc., by systematically adjusting those nonverbal factors. Human students sometimes make mistakes while practising a task, but teachers may be forgiving about them. Intelligent robots are machines, and therefore, they may behave erroneously in certain situations. Our second study examines if human teachers for a robot overlook its small mistakes made when practising a recently taught task, in case the robot has already shown significant improvements. By means of an online rating experiment (with 173 participants), we first determine how severe a robot’s errors in a household task (i.e., preparing food) are perceived. We then use that information to design and conduct another experiment (with 139 participants) in which participants are given the experience of teaching trainee robots. According to our results, perceptions of teachers improve as the robots get better in performing the task. We also show that while bigger errors have a greater negative impact on human teachers' trust compared with the smaller ones, even a small error can significantly destroy trust in a trainee robot. This effect is also correlated with the personality traits of participants. The present work contributes by extending HRI knowledge concerning human teachers’ understandings of robots, in a specific teaching scenario when teachers are observing behaviours that have the primary goal of accomplishing a physical task