AI & Integrity
What the AI is allowed to do, what it is forbidden to do, and how you can tell the difference. CitationLab resolves references against real records and asks a human before it changes anything; these posts explain where that line sits and why it is drawn there. If you want to know whether a tool can invent a reference into your thesis, start here.
Can AI invent fake references? Ours can't — by construction
The operation that produces fabricated references — generate a plausible source — is not implemented. What the model is actually asked, where references come from instead, and the one guard we removed on purpose.
ChatGPT gave you citations that don't exist. Now what?
AI-invented references look perfect and cite nothing. The five tells that expose a hallucinated citation, how to verify one in under two minutes, and what to do with the claim it was propping up.
Deterministic first, AI second — and why the order is the whole design
Why CitationLab settles what it can with rules before any model is consulted, and what that ordering buys you in a reference check you can actually verify.
It missed 27 citations, then told me it had found the last one
A general-purpose assistant missed 27 citations in one thesis chapter and said each time that it had found the last. That failure is why CitationLab runs rules before models.
Ref[In] — naming the thing a citation check is actually for
Every tool describes an action: run a check. REFerence [IN]tegrity names the property a check is for — three claims about your document, and a ledger that has to balance.
Why every correction needs your click
Why CitationLab never applies an AI-formed correction on its own: the click is what turns a machine suggestion into a decision you can account for.