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Do people rely on ChatGPT more than their peers to detect deepfake news?

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  • Yuhao Fu
  • Nobuyuki Hanaki

Abstract

This experimental study investigates how people rely on different sources of advice when detecting AI-generated fake news (deepfake news). In a laboratory deepfake detection task, student participants identified the proportion of human-written (non-AI-generated) content in synthetic deepfake news articles and received advice from ChatGPT (GPT-4), human peers, or linguistic experts. The results show that participants rely more on ChatGPT than on human peers when detecting GPT-2-generated deepfake news. Participants also rely more on linguistic experts than on peers, while the relative reliance on experts versus ChatGPT is mixed across experimental waves, potentially reflecting time trends in beliefs about AI-based detection. Moreover, performance improvements reflect the joint role of reliance and advice quality, arising primarily when participants rely on high-quality advice. Overall, relying on AI to detect AI-generated deepfakes can improve detection outcomes, but only when AI-based detection tools are of sufficiently high quality. These findings highlight the dual role of GAI as both a source of deepfakes and a tool for mitigating related risks.

Suggested Citation

  • Yuhao Fu & Nobuyuki Hanaki, 2024. "Do people rely on ChatGPT more than their peers to detect deepfake news?," ISER Discussion Paper 1233rr, Institute of Social and Economic Research, The University of Osaka, revised Feb 2026.
    • Handle: RePEc:dpr:wpaper:1233rr
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    References listed on IDEAS

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    4. Hütter, Mandy & Ache, Fabian, 2016. "Seeking advice: A sampling approach to advice taking," Judgment and Decision Making, Cambridge University Press, vol. 11(4), pages 401-415, July.
    5. Schultze, Thomas & Rakotoarisoa, Anne-Fernandine & Stefan, Schulz-Hardt, 2015. "Effects of distance between initial estimates and advice on advice utilization," Judgment and Decision Making, Cambridge University Press, vol. 10(2), pages 144-171, March.
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