应对生成式人工智能虚假信息 科学证据荟萃分析结果

The report finds that text-based GenAI misinformation currently poses a higher persuasive risk than visual misinformation. It identifies preemptive debunking as the most consistent intervention for reducing perceived credibility, while content labeling is effective only when standardized. The analysis highlights a critical evidence gap, as existing research is heavily concentrated in English-speaking and high-income countries.

• Content labeling (adding visual or text tags to indicate AI generation) can reduce the perceived credibility of misinformation, but its effectiveness is inconsistent and highly dependent on design, wording, and implementation. The report notes that the impact of labels varies significantly across different companies and scenarios.

APA

A.Herasimenka, Valenzuela, S., Boulianne, S., Esser, F., M.Given, L., Lewandowsky, S., M.Navarro-Lopez, E., & N.Howard, P. (2026). 应对生成式人工智能虚假信息 科学证据荟萃分析结果. International Panel on the Information Environment. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d3107d35aa999ef186b_SFP%20CHINESE%20FINAL.pdf

Chicago

A.Herasimenka, S. Valenzuela, S. Boulianne, F. Esser, L. M.Given, S. Lewandowsky, E. M.Navarro-Lopez, and P. N.Howard. 应对生成式人工智能虚假信息 科学证据荟萃分析结果. International Panel on the Information Environment, 2026. https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d3107d35aa999ef186b_SFP%20CHINESE%20FINAL.pdf.

Wikipedia

{{cite report |last1=A.Herasimenka |last2=Valenzuela |first2=S. |last3=Boulianne |first3=S. |last4=Esser |first4=F. |last5=M.Given |first5=L. |last6=Lewandowsky |first6=S. |last7=M.Navarro-Lopez |first7=E. |last8=N.Howard |first8=P. |title=应对生成式人工智能虚假信息 科学证据荟萃分析结果 |publisher=International Panel on the Information Environment |date=February 2026 |url=https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d3107d35aa999ef186b_SFP%20CHINESE%20FINAL.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{aherasimenka2026, author = {A.Herasimenka and Valenzuela, S. and Boulianne, S. and Esser, F. and M.Given, L. and Lewandowsky, S. and M.Navarro-Lopez, E. and N.Howard, P.}, title = {{应对生成式人工智能虚假信息 科学证据荟萃分析结果}}, institution = {International Panel on the Information Environment}, year = {2026}, month = feb, url = {https://cdn.prod.website-files.com/643ecb10be528d2c1da863cb/6a3b8d3107d35aa999ef186b_SFP%20CHINESE%20FINAL.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }