Not all AI-generated content carries the same disinformation risk. A labelled, sourced summary on a low-reach channel is very different from an unlabelled, mass-produced claim pushed to a large, trusting audience. The AI Disinformation Risk Analyzer scores the structural factors that turn AI output into a disinformation hazard and suggests concrete mitigations.
How it works
You describe the AI system or the specific piece of generated content, then characterise the audience and distribution channel. The tool scores five factors that research and regulators (including the EU Code of Practice on Disinformation) consistently identify: verifiability of the claims, source transparency, whether AI involvement is disclosed, the manipulation/scale potential, and the reach and trust of the audience and channel.
These combine into an overall risk profile — low, moderate, or high — with a short rationale and a prioritised list of mitigations for the factors that scored worst.
What it measures
High verifiability (claims can be checked against named sources) lowers risk; unverifiable factual claims raise it. Hidden AI generation, cheap mass production, and large low-trust audiences on fast viral channels all push risk up. A piece that is clearly labelled, well-sourced, human-reviewed, and aimed at a small informed audience scores low even if it discusses sensitive topics.
Tips and notes
The single highest-leverage mitigation is almost always disclosure — labelling content as AI-generated and attaching provenance metadata (C2PA) — because it costs little and directly addresses the deception vector. The second is human review before high-reach publication. A low score is never a substitute for fact-checking the actual claims; it only tells you the structural risk is contained. Everything runs locally in your browser and nothing is uploaded.