BIASsist: Empowering News Readers via Bias Identification, Explanation, and Neutralization

要旨

Biased news articles can distort readers' perceptions by presenting information in a way that favors or disfavors a particular point of view. Subtly embedded in the text, these biased news articles can shape our views daily without people even realizing it. To address this issue, we propose BIASsist, an LLM-based approach designed to mitigate bias in news articles. Based on existing research, we defined six types of bias and introduced three assistive components—identification, explanation, and neutralization—to provide a broader range of bias information and enhance readers' bias-awareness. We conducted a mixed-method study with 36 participants to evaluate the effectiveness of BIASsist. The results show participants' bias awareness significantly improved and their interest in identifying bias increased. Participants also tended to engage more actively in critically evaluating articles. Based on these findings, we discuss its potential to improve media literacy and critical thinking in today's information overload era.

著者
Yeo-Gyeong Noh
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
MinJu Han
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
Junryeol Jeon
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
Jin-Hyuk Hong
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
DOI

10.1145/3706598.3713531

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713531

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: Decision Making and Analysis

G414+G415
6 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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