Scaling Truth: The Confidence Paradox in AI Fact-Checking

• URL: http://arxiv.org/abs/2509.08803v1
• Date: Wed, 10 Sep 2025 17:36:25 GMT
• Title: Scaling Truth: The Confidence Paradox in AI Fact-Checking
• Authors: Ihsan A. Qazi, Zohaib Khan, Abdullah Ghani, Agha A. Raza, Zafar A. Qazi, Wassay Sajjad, Ayesha Ali, Asher Javaid, Muhammad Abdullah Sohail, Abdul H. Azeemi,
• Abstract summary: Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain.<n>We systematically evaluate nine established LLMs across multiple categories using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages.<n>Findings reveal a concerning pattern resembling the Dunning-Kruger effect: smaller, models show high confidence despite lower accuracy, while larger models demonstrate higher accuracy but lower confidence.
• Score: 0.8201655885319955
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages. Our methodology tests model generalizability on claims postdating training cutoffs and four prompting strategies mirroring both citizen and professional fact-checker interactions, with over 240,000 human annotations as ground truth. Findings reveal a concerning pattern resembling the Dunning-Kruger effect: smaller, accessible models show high confidence despite lower accuracy, while larger models demonstrate higher accuracy but lower confidence. This risks systemic bias in information verification, as resource-constrained organizations typically use smaller models. Performance gaps are most pronounced for non-English languages and claims originating from the Global South, threatening to widen existing information inequalities. These results establish a multilingual benchmark for future research and provide an evidence base for policy aimed at ensuring equitable access to trustworthy, AI-assisted fact-checking.

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