Abstract
In AI-driven decision support systems, explanations are essential for users to assess the system's suggestions. With the advent of large language models, it has become significantly easier to tailor the way explanations are expressed. However, the impact of these expressions on human decision-making remains largely unexplored. This study investigates how explanation tones, such as formal or humorous, influence decision-making, focusing on the roles of AI and user attributes. We conducted user experiments using three scenarios based on distinct AI roles: assistant, second-opinion provider, and expert. The results revealed that in the second-opinion scenario, explanation tone had a significant impact on decision-making regardless of user attributes. In contrast, in the assistant and expert scenarios, the influence of tone varied depending on user attributes. Older users were found to be more susceptible to tone influences, while highly extroverted users tended to exhibit discrepancies between their perceptions and decisions. This study offers valuable insights for designing effective explanation styles in AI systems.
