Study Highlights Gender Bias in LLM-Based Fake News Detection
A recent study has revealed significant gender bias in large language models (LLMs) used for fake news detection. The research, conducted by Razieh Chalehchaleh and colleagues, systematically investigates how LLMs assign different veracity labels to identical statements based solely on the perceived gender of the speaker. The study augmented the LIAR benchmark dataset with gender variants of speaker job titles to test this bias. Results showed that all models exhibited gender sensitivity, with 9.79% to 35.13% of statements receiving inconsistent labels across gender variants. The study identified two primary bias manifestations: instability and directionality, with some models showing a male-skeptic pattern. These findings underscore the need for bias-aware evaluation and mitigation strategies in LLM-based fact-checking. 
