Respuesta a la desinformación generada por inteligencia artificial generativa Resultados de un metaanálisis de la evidencia científica

The report finds that generative AI-produced textual misinformation currently poses a higher persuasion risk than visual misinformation. It identifies preventive corrective information as the most consistently effective intervention for reducing the perceived credibility of such content, while content labeling shows promising but inconsistent results depending on design. The analysis highlights a significant global evidence gap, as most research is concentrated in high-income, English-speaking countries.

• Textual misinformation generated by generative AI is currently perceived as more accurate, credible, or plausible than true information, posing a more immediate persuasive risk than visual misinformation such as deepfakes.
• Preventive corrective information—such as educational warnings or reminders provided before exposure—consistently reduces the perceived accuracy, credibility, or acceptance of generative AI misinformation with a small to moderate effect size.
• Content labeling can reduce the perceived credibility of misleading generative AI information, but its effectiveness varies significantly based on design, wording, presentation, and the source of the label.
• There is a substantial global knowledge gap because the current evidence base is heavily concentrated on high-income, English-speaking countries, leaving many regions, languages, and political systems under-researched.
• The meta-analysis was based on 60 effect estimates from randomized controlled trials extracted from 24 peer-reviewed publications involving 33,801 people, published between 2018 and 2025.

APA

Herasimenka, A., Valenzuela, S., Boulianne, S., Esser, F., Given, L. M., Lewandowsky, S., Navarro-Lopez, E. M., & Howard, P. N. (2026). Respuesta a la desinformación generada por inteligencia artificial generativa Resultados de un metaanálisis de la evidencia científica. International Panel on the Information Environment. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d4bd9700c3d09dad036_SFP%20SPANISH%20FINAL.pdf

Chicago

Herasimenka, A., S. Valenzuela, S. Boulianne, F. Esser, L. M. Given, S. Lewandowsky, E. M. Navarro-Lopez, and P. N. Howard. Respuesta a la desinformación generada por inteligencia artificial generativa Resultados de un metaanálisis de la evidencia científica. International Panel on the Information Environment, 2026. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d4bd9700c3d09dad036_SFP%20SPANISH%20FINAL.pdf.

Wikipedia

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BibTeX

@techreport{herasimenka2026respuesta, 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 = {{Respuesta a la desinformación generada por inteligencia artificial generativa Resultados de un metaanálisis de la evidencia científica}}, institution = {International Panel on the Information Environment}, year = {2026}, url = {https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d4bd9700c3d09dad036_SFP%20SPANISH%20FINAL.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }