AI Insight
This study surveyed 21 experts in AI, policy, and disinformation to assess threats from GenAI-produced fake content across different media types. While deepfake videos generate immediate concern, experts identified large-scale automated text generation as the more systemic threat, capable of creating "epistemic fragmentation" and false consensus, particularly in political contexts. Experts expressed skepticism about technical detection methods, favoring provenance tracking standards and regulatory approaches, though implementation challenges remain significant.
Why it matters
The findings highlight a critical gap between public concern about obvious deepfakes and the potentially more damaging threat of scaled text-based disinformation that can systematically erode information integrity. The call for standardized benchmarks and reproducible research methods addresses a fundamental challenge in measuring and countering synthetic media manipulation.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats — text, image, audio, and video — and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate “shock” value, large-scale text generation poses a systemic risk of “epistemic fragmentation” and “synthetic consensus,” particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.