Реагирование на дезинформацию, создаваемую генеративным ИИ

The report analyzes 60 effect estimates from 24 peer-reviewed studies (33,801 participants) published between 2018 and 2025 to evaluate generative AI (GenAI) misinformation. It finds that GenAI-generated text currently poses a higher risk of persuasive impact than visual misinformation. The most consistently effective countermeasure is the preemptive provision of corrective information, while content labeling shows variable effectiveness depending on design and application. The authors highlight a significant global knowledge gap, as research is heavily concentrated in English-speaking, high-income countries.

• Text-based misinformation created by GenAI is currently perceived as more accurate, credible, or plausible than authentic information, posing a higher risk of persuasive impact than visual misinformation like deepfakes.
• The current scientific evidence base is geographically limited, focusing primarily on English-language publications and high-income countries, including Austria, Germany, the Netherlands, South Korea, the UK, and the USA, leaving a significant knowledge gap for other regions and languages.
• Preemptive corrective information—such as educational warnings or psychological 'inoculation' provided before exposure to content—is the most consistently effective intervention for reducing the perceived accuracy and credibility of GenAI misinformation.
• Content labeling can reduce the perceived credibility of GenAI misinformation, but its effectiveness is inconsistent and highly dependent on the label's design, wording, presentation, and the source of the label.
• Scientific research often lags behind the rapid pace of technological development, with many experiments testing outdated systems like GPT-2 or early tools like FaceSwap rather than current state-of-the-art models.

APA

Herasimenka, A., Valenzuela, S., Boulianne, S., Esser, F., Given, L. M., Lewandowsky, S., Navarro-Lopez, E. M., & Howard, P. N. (2026). Реагирование на дезинформацию, создаваемую генеративным ИИ. International Panel on the Information Environment. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d51f8e4588d0472af23_SFP%20RUSSIAN%20FINAL.pdf

Chicago

Herasimenka, A., S. Valenzuela, S. Boulianne, F. Esser, L. M. Given, S. Lewandowsky, E. M. Navarro-Lopez, and P. N. Howard. Реагирование на дезинформацию, создаваемую генеративным ИИ. International Panel on the Information Environment, 2026. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d51f8e4588d0472af23_SFP%20RUSSIAN%20FINAL.pdf.

Wikipedia

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BibTeX

@techreport{herasimenka2026, author = {Herasimenka, A. and Valenzuela, S. and Boulianne, S. and Esser, F. and Given, L. M. and Lewandowsky, S. and Navarro-Lopez, E. M. and Howard, P. N.}, title = {{Реагирование на дезинформацию, создаваемую генеративным ИИ}}, institution = {International Panel on the Information Environment}, year = {2026}, url = {https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d51f8e4588d0472af23_SFP%20RUSSIAN%20FINAL.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }