In January 2025, a university professor submitted a sworn declaration to a federal court. He was an expert on AI misinformation. The filing was about the dangers of AI-generated content.
It contained three hallucinated citations — generated by the very ChatGPT he was warning the court about.
Judge Laura M. Provinzino called it "the irony." Professor Hancock — credentialed, published, credible on the stand — had been fooled by his own tool. The citations attributed articles to authors who had never written them, on topics slightly adjacent to his actual expertise.
This is not an edge case. It's the defining verification challenge of our moment.
Why Traditional Fact-Checking Breaks Down
Before we get into the framework, let's be clear about what's different.
Traditional fact-checking works when there's a traceable source. A newspaper article has a publication date, an author, an outlet with editorial standards. A research paper has a journal, peer review, a DOI. A government report has an agency, a contact, a PDF.
You can look up the author. You can check the publication. You can verify the outlet exists and has standards.
AI content has none of this.
When a language model generates a confident claim — "The FDA approved this drug in 2023" or "Case law establishes that X" — there is no author, no publication date, no institutional accountability. There is only text that sounds like it could have come from a credible source.
The tool does not know it is lying. It has no ground truth. It generates the most statistically likely continuation of the prompt, regardless of whether that continuation is true.
This is what we call hallucination. And it is structurally different from the typos, biases, or oversights you find in human-authored content.
The Librarian's Verification Framework
Here's what to do instead. I've organized this around five moves — each one borrowed from how professional fact-checkers and research librarians evaluate sources they've never seen before.
1. Lateral Reading — Leave the Page
The most effective fact-checking technique professional fact-checkers use is called lateral reading. The core insight: instead of analyzing the source in front of you, you leave it and check what independent, trusted sources say about it.
With AI content, this means asking a different question than you would with a traditional source.
Traditional source: Who created this?
AI output: Who else is saying this?
When you encounter a claim from an AI tool, fractionate it — break the output into individual assertions — and open a new tab. Search for each claim by its key terms. Look for corroboration from news organizations, academic databases, government sites, or established fact-checking organizations (Duke Reporters Lab, PolitiFact, Africa Check, Full Fact).
A useful rule of thumb: the Rule of Three. Find at least three independent, credible sources that confirm the same fact. If you can't find any, treat the claim as suspect.
2. Verify Citations Before You Trust Them
Hallucinated citations are the single clearest signal that you're dealing with AI-generated content that may be unreliable.
This is not just a theoretical concern. The Damien Charlotin AI Hallucination Cases database has logged over 160 documented cases since 2023. The Johnson v. Dunn case (N.D. Ala., July 2025) resulted in sanctions against a large law firm for submitting a brief containing hallucinated case citations — generated by the same AI the firm had explicitly warned its attorneys not to use.
When an AI tool gives you a case citation, a journal article, a statistic attributed to a study — verify it. Search for the case name, the article title, the author. Check whether the work actually exists. If the citation doesn't appear in a reputable legal database, academic index, or news archive, the AI invented it.
This step is non-negotiable in any professional or academic context.
3. Check the Date — and Look for What's Newer
AI training has a cutoff date. When you ask about recent developments, the model's answer is often based on training data that predates the event you're asking about.
Before accepting any AI claim about a law, a standard, a technology, or a policy — ask: is this current? Search for the most recent update on this topic. If your search finds a 2025 or 2026 source that contradicts an AI output that may have been trained on 2023 data, trust the newer source.
This is especially important for technical content. The AI may confidently recommend a library version that was deprecated two years ago, cite a specification that was superseded, or describe a regulatory framework that has since changed.
4. Assess the Source — or the Absence of One
Every credible source has a chain of accountability. An author, an institution, a publication, a date, a contact. You can look these up. You can evaluate whether they have expertise, what their incentives are, whether their track record is solid.
When AI generates content, that chain is often missing. There is no author, no institution, no editorial process. Just text.
Source triangulation replaces the traditional authority check. If the AI makes a claim about medical evidence, find the actual medical journal. If it cites a legal standard, find the actual statute or case. If it describes a technical specification, go to the standards body or the primary source.
Look for: Who else is saying this? If the claim appears nowhere in the domain's credible literature — not in a journal, not in an official publication, not in an established reference — treat it as suspect.
5. Look for Provenance Signals (When Applicable)
For images, audio, and video, C2PA (Coalition for Content Provenance and Authenticity) Content Credentials are beginning to provide cryptographic provenance metadata — a tamper-evident record of whether a file was created by a camera, edited in software, or generated by an AI model.
This only works for content that implements C2PA at the source. OpenAI, Adobe, Google, and Meta now embed these manifests in AI-generated content. If an image carries a Content Credentials badge, you can verify its origin by visiting contentcredentials.org/verify.
For text content, this step doesn't apply — but the preceding four steps do, and they're more than sufficient.
A Practical Workflow
Here's how this plays out in practice:
- Get the AI output. Note the specific claims, not the overall impression.
- Leave the chat. Open new tabs. Don't analyze — search.
- Search each claim by key terms. Apply the Rule of Three.
- Check the date. Look for 2025–2026 sources. Trust newer over older.
- Verify citations. Pull the actual case, article, or study.
- Triangulate. Find the credible domain literature. If it's not there, the claim is suspect.
- Apply CRAAP (Currency, Relevance, Authority, Accuracy, Purpose) to everything you find.
How Sabia Fits Into This
This framework — lateral reading, citation verification, source triangulation, CRAAP — is exactly what I use when evaluating a source through Sabia's evaluator. The tool doesn't just check whether content sounds credible; it applies these structural checks and flags when claims lack corroboration or when citations may be fabricated.
If you want a structured way to apply this framework to the AI outputs you encounter, sabialibrarian.com is built for exactly that.
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