Répondre à la désinformation générée par l’IA générative

The document analyzes the impact of generative AI misinformation, finding that textual misinformation currently poses a higher persuasion risk than visual misinformation. It highlights a significant global data gap, as research is heavily concentrated in high-income, English-speaking countries. Among countermeasures, preventive corrective information is identified as the most consistent intervention for reducing perceived credibility, while content labeling is effective only when applied consistently and with rigorous design.

• Text-based misinformation generated by AI is currently more persuasive and poses a greater risk than visual misinformation. While deepfakes remain a risk, recent data indicates that AI-generated fake texts are often perceived as more accurate and credible than truthful information, whereas AI-generated visual misinformation is often seen as less credible than truth.
• There is a critical lack of global data on generative AI misinformation, with existing research predominantly focused on high-income, English-speaking nations. This creates a knowledge gap regarding how such misinformation operates in different linguistic, cultural, and political environments.
• Preventive corrective information—such as educational warnings or prompts provided before exposure—is the most consistently effective intervention for reducing the perceived accuracy, credibility, and acceptance of AI-generated misinformation.
• Content labeling can reduce the perceived credibility of misleading AI information, but its effectiveness is inconsistent and highly dependent on the design, wording, and presentation of the labels.

APA

Herasimenka, A., Valenzuela, S., Boulianne, S., Esser, F., Given, L. M., Lewandowsky, S., Navarro-Lopez, E. M., & Howard, P. N. (2026). Répondre à la désinformation générée par l’IA générative. International Panel on the Information Environment. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d43c6f80f520fa37a13_SFP%20FRENCH%20FINAL.pdf

Chicago

Herasimenka, A., S. Valenzuela, S. Boulianne, F. Esser, L. M. Given, S. Lewandowsky, E. M. Navarro-Lopez, and P. N. Howard. Répondre à la désinformation générée par l’IA générative. International Panel on the Information Environment, 2026. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d43c6f80f520fa37a13_SFP%20FRENCH%20FINAL.pdf.

Wikipedia

{{cite report |last1=Herasimenka |first1=A. |last2=Valenzuela |first2=S. |last3=Boulianne |first3=S. |last4=Esser |first4=F. |last5=Given |first5=L. M. |last6=Lewandowsky |first6=S. |last7=Navarro-Lopez |first7=E. M. |last8=Howard |first8=P. N. |title=Répondre à la désinformation générée par l’IA générative |publisher=International Panel on the Information Environment |date=2026 |url=https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d43c6f80f520fa37a13_SFP%20FRENCH%20FINAL.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{herasimenka2026rpondre, 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 = {{Répondre à la désinformation générée par l’IA générative}}, institution = {International Panel on the Information Environment}, year = {2026}, url = {https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d43c6f80f520fa37a13_SFP%20FRENCH%20FINAL.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }