AI output sourcing transparency checklist
AI can draft a research brief in seconds — and quietly attach citations that don’t exist, don’t match the claim, or are years out of date. This checklist walks you through the specific failure modes of machine-generated sourcing so that anything you publish or rely on is actually traceable to a real, accurate source. Pick your standard, verify each item, and get a transparency score plus the exact gaps to close.
How it works
The checklist separates four things that fail independently: whether each cited source exists, whether it actually supports the claim attached to it, whether every factual claim is grounded in a citation at all, and whether sources are current enough for the topic. Higher standards (journalistic, academic) require more items and add provenance and conflict-of-interest checks; the general professional standard keeps the core grounding and accuracy checks but relaxes formatting. Required items carry more weight than recommended ones, so the score drops sharply if you skip something fundamental like verifying that a citation says what the text claims it does.
Tips and notes
- Click every link. The most common AI sourcing failure is a real source cited for a claim it never makes — only opening it catches this.
- Watch for fabricated DOIs and URLs. Plausible-looking identifiers that 404 or resolve to something unrelated are a hallmark of generated citations.
- Ground every number. Statistics with no source are the highest-risk claims; treat an unsourced figure as unverified.
- A clean score is about traceability, not truth. Use it alongside your own judgement of whether the conclusions follow from the sources.