Feeds of Distrust: Investigating How AI-Powered News Chatbots Shape User Trust and Perceptions

The start of the 2020s ushered in a new era of AI through the rise of Generative AI Large Language Models (LLMs) such as ChatGPT. These AI chatbots offer a form of interactive agency by enabling users to ask questions and query for more information. However, prior research only considers if LLMs have a political bias or agenda, and not how a biased LLM can impact a user’s opinion and trust. Our study bridges this gap by investigating a scenario where users read online news articles and then engage with an interactive AI chatbot, where both the news and the AI are biased to hold a particular stance on a news topic. Interestingly, participants were far more likely to adopt the narrative of a biased chatbot over news articles with an opposing stance. Participants were also substantially more inclined to adopt the chatbot’s narrative if its stance aligned with the news—all compared to a control news-article only group. Our findings suggest that the very interactive agency offered by an AI chatbot significantly enhances its perceived trust and persuasive ability compared to the ‘ static ’ articles from established news outlets, raising concerns about the potential for AI-driven indoctrination. We outline the reasons behind this phenomenon and conclude with the implications of biased LLMs for HCI research, as well as the risks of Generative AI undermining democratic integrity through AI-driven Information Warfare.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Interactive Intelligent Systems
Published
2025-03-14
DOI
https://doi.org/10.1145/3722227
Citations
3
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
7.44
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Feeds of Distrust: Investigating How AI-Powered News Chatbots Shape User Trust and Perceptions

Jorge Gonçalves, Jarod Govers, Eduardo Velloso, Saumya Pareek
3 citations
ACM Transactions on Interactive Intelligent Systems
AI in Service Interactions
7.44
article

Feeds of Distrust: Investigating How AI-Powered News Chatbots Shape User Trust and Perceptions

Jorge Gonçalves, Jarod Govers, Eduardo Velloso, Saumya Pareek
article en
3 citations

Abstract

The start of the 2020s ushered in a new era of AI through the rise of Generative AI Large Language Models (LLMs) such as ChatGPT. These AI chatbots offer a form of interactive agency by enabling users to ask questions and query for more information. However, prior research only considers if LLMs have a political bias or agenda, and not how a biased LLM can impact a user’s opinion and trust. Our study bridges this gap by investigating a scenario where users read online news articles and then engage with an interactive AI chatbot, where both the news and the AI are biased to hold a particular stance on a news topic. Interestingly, participants were far more likely to adopt the narrative of a biased chatbot over news articles with an opposing stance. Participants were also substantially more inclined to adopt the chatbot’s narrative if its stance aligned with the news—all compared to a control news-article only group. Our findings suggest that the very interactive agency offered by an AI chatbot significantly enhances its perceived trust and persuasive ability compared to the ‘ static ’ articles from established news outlets, raising concerns about the potential for AI-driven indoctrination. We outline the reasons behind this phenomenon and conclude with the implications of biased LLMs for HCI research, as well as the risks of Generative AI undermining democratic integrity through AI-driven Information Warfare.

ACM Transactions on Interactive Intelligent SystemsVol. 15(4)
The University of Sydney (AU), The University of Melbourne (AU)
Openalex Percentile: Top 4%
AI in Service Interactions
7.44
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.