Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making

要旨

Explanatory information helps users to evaluate the suggestions offered by AI-driven decision support systems. With large language models, adjusting explanation expressions has become much easier. However, how these expressions influence human decision-making remains largely unexplored. This study investigated the effect of explanation tone (e.g., formal or humorous) on decision-making, focusing on AI roles and user attributes. We conducted user experiments across three scenarios depending on AI roles (assistant, second-opinion provider, and expert) using datasets designed with varying tones. The results revealed that tone significantly influenced decision-making regardless of user attributes in the second-opinion scenario, whereas its impact varied by user attributes in the assistant and expert scenarios. In addition, older users were more influenced by tone, and highly extroverted users exhibited discrepancies between their perceptions and decisions. Furthermore, open-ended questionnaires highlighted that users expect tone adjustments to enhance their experience while emphasizing the importance of tone consistency and ethical considerations. Our findings provide crucial insights into the design of explanation expressions.

著者
Ayano Okoso
Toyota Central R&D labs., Inc., Aichi, Japan
Mingzhe Yang
The University of Tokyo, Tokyo, Japan
Yukino Baba
The University of Tokyo, Tokyo, Japan
DOI

10.1145/3706598.3713744

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713744

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Personal Data and Decision-Making

Annex Hall F204
6 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
日本語まとめ
読み込み中…