Understanding the Effects of AI-based Credibility Indicators When People Are Influenced By Both Peers and Experts

要旨

In an era marked by rampant online misinformation, artificial intelligence (AI) technologies have emerged as tools to combat this issue. This paper examines the effects of AI-based credibility indicators in people’s online information processing under the social influence from both peers and "experts''. Via three pre-registered, randomized experiments, we confirm the effectiveness of accurate AI-based credibility indicators to enhance people's capability in judging information veracity and reduce their propensity to share false information, even under the influence from both laypeople peers and experts. Notably, these effects remain consistent regardless of whether experts' expertise is verified, with particularly significant impacts when AI predictions disagree with experts. However, the competence of AI moderates the effects, as incorrect predictions can mislead people. Furthermore, exploratory analyses suggest that under our experimental settings, the impact of the AI-based credibility indicator is larger than that of the expert's. Additionally, AI's influence on people is partially mediated through peer influence, although people automatically discount the opinions of their laypeople peers when seeing an agreement between AI and peers' opinions. We conclude by discussing the implications of utilizing AI to combat misinformation.

著者
Zhuoran Lu
Purdue University, West Lafayette, Indiana, United States
Patrick Li
Purdue University, West Lafayette, Indiana, United States
Weilong Wang
Purdue University, West Lafayette, Indiana, United States
Ming Yin
Purdue University, West Lafayette, Indiana, United States
DOI

10.1145/3706598.3713871

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713871

動画

会議: CHI 2025

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

セッション: Perception of Systems

G401
5 件の発表
2025-04-29 18:00:00
2025-04-29 19:30:00
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