Decoding AI Judgment: How LLMs Assess News Credibility and Bias

• URL: http://arxiv.org/abs/2502.04426v2
• Date: Thu, 10 Jul 2025 14:31:21 GMT
• Title: Decoding AI Judgment: How LLMs Assess News Credibility and Bias
• Authors: Edoardo Loru, Jacopo Nudo, Niccolò Di Marco, Alessandro Santirocchi, Roberto Atzeni, Matteo Cinelli, Vincenzo Cestari, Clelia Rossi-Arnaud, Walter Quattrociocchi,
• Abstract summary: Large Language Models (LLMs) are increasingly embedded in that involve evaluative processes.<n>This raises the need to examine how such evaluations are built, what assumptions they rely on, and how their strategies diverge from those of humans.<n>We benchmark six LLMs against expert ratings--NewsGuard and Media Bias/Fact Check (MBFC)--and against human judgments collected through a controlled experiment.
• Score: 33.7054351451505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly embedded in workflows that involve evaluative processes. This raises the need to examine how such evaluations are built, what assumptions they rely on, and how their strategies diverge from those of humans. We benchmark six LLMs against expert ratings--NewsGuard and Media Bias/Fact Check (MBFC)--and against human judgments collected through a controlled experiment. To enable direct comparison, we implement a structured agentic framework in which both models and non-expert participants follow the same evaluation procedure: selecting criteria, retrieving content, and producing justifications. Despite output alignment, LLMs rely on different mechanisms: lexical associations and statistical priors replace contextual reasoning. This reliance produces systematic effects: political asymmetries, opaque justifications, and a tendency to confuse linguistic form with epistemic validity. Delegating judgment to such systems does not merely automate evaluation--it redefines it, shifting from normative reasoning to pattern-based approximation.

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