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Evidence record 16276 · automatically gathered

Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection

Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored. This study presents the first systematic investigation of gender bias in LLM-based fake news detection using real-world data. We augment the LIAR benchmark with three gender variants of speaker job titles (Neutral, Male, Female) for each statement to test whether veracity judgments vary solely based on gender presentation. Six state-of-the

Record details

Published: 4 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Jobs & economy · Misinformation · Finance, VC & PE
Retrieved: 5 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (4 August 2026), “Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection,” evidence record 16276, https://ethics.ai/record/16276 (originally published by arXiv).

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