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Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions.

Updated: Sep 27

Zhou J, Zhang Y, Luo Q, Parker AG, De Choudhury M. Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023; Article No. 436:1-20. https://doi.org/10.1145/3544548.3581318


Summary: This study examines AI-generated misinformation (AI-misinfo) by comparing it to human-created misinformation, focusing on linguistic characteristics and the effectiveness of current detection methods. Using COVID-19 misinformation as a basis, AI-misinfo was generated and analyzed, revealing differences in how AI enhances details, communicates uncertainties, and uses personal tones. Despite some success, existing models showed a significant drop in performance when detecting AI-misinfo compared to human-misinfo. Furthermore, current information assessment guidelines, such as credibility and source transparency, were less effective for AI-misinfo. The study highlights the growing challenge AI poses in combatting misinformation.

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