Expert survey on GenAI disinformation - the shared instrument behind our publications on threat perception and mitigation
"We are at a critical inflection point. GenAI has reduced the cost of producing disinformation to near-zero. Your expertise matters."
We are conducting a longitudinal research study to understand how experts perceive the evolving threat landscape of AI-generated disinformation. This is Wave 2 of our ongoing study—your voice will directly shape policy recommendations and reproducible mitigation frameworks.
We are seeking domain experts with professional experience in:
| Domain | Examples |
|---|---|
| AI/ML Research & Engineering | Researchers working on LLMs, diffusion models, synthetic media detection |
| Cybersecurity | Threat intelligence, adversarial ML, platform security |
| Digital Policy & Regulation | Policymakers, regulators, governance specialists (EU AI Act, DSA, etc.) |
| Journalism & Fact-Checking | Investigative journalists, fact-checkers, media analysts |
| Computational Social Science | Researchers studying online behavior, misinformation dynamics |
| Ethics & Law | AI ethicists, legal scholars focused on synthetic media |
- Shape the research: Your insights directly inform academic findings and policy recommendations
- GDPR-compliant & anonymous: Responses are anonymized; optional attribution in acknowledgments
- Time commitment: Approximately 15 minutes
- Stay informed: Receive a summary of findings upon publication
The expert survey questionnaire is available in two formats:
- Online: Google Forms
- Printable PDF: survey-print.pdf (generated from LaTeX source)
| Concept | Definition |
|---|---|
| Verification Crisis | The structural shift where GenAI reduces the cost of producing high-fidelity disinformation toward zero, risking the erosion of shared factual basis in democratic deliberation |
| Epistemic Fragmentation | The breakdown of a shared reality as personalized synthetic content creates isolated information bubbles |
| Synthetic Consensus | The artificial manufacture of apparent agreement through AI-generated content simulating public opinion |
| Reproducible Provenance | Transparent, standardized infrastructure for verifying information origins—treating information integrity as infrastructure |
The survey ran with 21 domain experts and 58 variables (7-point Likert scales, Best-Worst Scaling, open-ended responses). Two deposits exist, with different scopes and access models.
| Deposit | Contents | Access |
|---|---|---|
Harvard Dataverse 10.7910/DVN/BXO2QA |
De-identified responses, aggregated tables, and the survey instrument, as published with the HKS Misinformation Review article | Open, CC0 1.0 |
Zenodo 10.5281/zenodo.18703601 |
Full Wave 1 response data underlying the WWW '26 analysis | Restricted to academic research; request via Zenodo |
The Dataverse deposit is open because it carries no personal data. The Zenodo deposit retains material that cannot be released openly, so access is granted on request.
This repository holds the survey instrument. The peer-reviewed journal article reporting the survey findings is the preferred citation:
@article{loth2026hksexperts,
author = {Loth, Alexander and Kappes, Martin and Pahl, Marc-Oliver},
title = {Experts Disagree on How to Fight {AI} Disinformation,
but Agree That Health and Politics Need Different Solutions},
journal = {Harvard Kennedy School Misinformation Review},
volume = {7},
number = {4},
year = {2026},
month = jul,
doi = {10.37016/mr-2020-205},
url = {https://doi.org/10.37016/mr-2020-205}
}If you draw on the Wave 1 analysis specifically, please cite the WWW '26 Companion paper as well:
@inproceedings{loth2026verification,
author = {Loth, Alexander and Kappes, Martin and Pahl, Marc-Oliver},
title = {The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance},
booktitle = {Companion Proceedings of the ACM Web Conference 2026 (WWW '26 Companion)},
year = {2026},
month = jun,
publisher = {ACM},
address = {New York, NY, USA},
location = {Dubai, United Arab Emirates},
pages = {980--988},
doi = {10.1145/3774905.3795484},
url = {https://arxiv.org/abs/2602.02100}
}| Publication | Venue | Data |
|---|---|---|
| Experts Disagree on How to Fight AI Disinformation, but Agree That Health and Politics Need Different Solutions doi:10.37016/mr-2020-205 | HKS Misinformation Review 7(4), 2026 | Dataverse, open |
| The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance doi:10.1145/3774905.3795484 | WWW '26 Companion (R2CASS), Wave 1 | Zenodo, restricted |
Related work on human rather than expert perception, using a separate study design:
- Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News (WebSci Companion '26). doi:10.1145/3795513.3807431
- The Indistinguishability Threshold: Measuring Cognitive Vulnerabilities to AI-Generated Disinformation (WebSci Companion '26, PhD Symposium). doi:10.1145/3795513.3807421
Note: The published findings cover Wave 1 of this longitudinal study. Expert responses for Wave 2 are being collected now. Participate to contribute to future research.
- Alexander Loth — Frankfurt University of Applied Sciences, Germany
- Martin Kappes — Frankfurt University of Applied Sciences, Germany
- Marc-Oliver Pahl — IMT Atlantique, UMR IRISA, Chaire Cyber CNI, France
This research builds on the JudgeGPT research platform—open-source infrastructure for studying human perception of AI-generated content.
This project is licensed under the MIT License - see the LICENSE file for details.
We thank the R2CASS workshop organizers—Momeni, Bleier, Dessì, and Khan—for establishing the reproducibility frameworks that inform this research.
"We must treat information integrity as infrastructure. Just as we build roads and power grids, we must build the protocols for truth verification."
— Survey Respondent (Policy Advisor)
