Making a human's trust repair for an agent in a series of tasks through the agent's empathic behavior

Abstract

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As AI technology develops, trust in agents (including robot, AI and anthropomorphic agent) is becoming more important for more AI applications in human society. Possible ways to improve the trust relationship include empathy, success-failure series, and capability (performance). Appropriate trust is less likely to cause deviations between actual and ideal performance. In this study, we focus on the agent's empathic behavior and success-failure series to increase trust in agents. We experimentally examine the effect of empathic behavior from agent to person on changes in trust over time. The experiment was conducted with a two-factor mixed design: empathic behavior (available, not available) and success-failure series (phase 1 to phase 5). An analysis of variance (ANOVA) was conducted using data from 200 participants. The results showed an interaction between the empathic behavior factor and the success-failure series factor, with trust in the agent repairing when empathic behavior was present. This result supports our hypothesis. This study shows that designing agents to be empathic behavior is an important factor for trust and helps humans build appropriate trust relationships with agents.

Keywords

• human-agent interaction
• empathy agent
• empathy
• trust
• trust repair