🤖 AI Summary
This study addresses the systemic threat posed by generative AI–enabled multimodal disinformation to the information ecosystem, highlighting the lack of reproducibility and standardized benchmarks in current detection mechanisms. Through an initial longitudinal expert survey (N=21) involving AI researchers, policymakers, and disinformation specialists, the work synthesizes risk assessments of synthetic text, image, audio, and video content and evaluates the efficacy of existing mitigation strategies. Innovatively conceptualizing information integrity as critical infrastructure, the study proposes a response framework grounded in reproducible provenance standards and methodologies, advocating for standardized evaluation benchmarks and reproducibility checklists. Findings indicate that large-scale text generation presents greater systemic risk than deepfake videos, and experts broadly express skepticism toward purely technical detection approaches, favoring instead integrated governance solutions that combine provenance standards with regulatory frameworks.
📝 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.