The Tool Desk
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What you are actually checking
An AI research answer contains several different things that need different tests:
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- Factual claims: statements about events, numbers, people, laws, studies, or technical behavior.
- Interpretations: explanations of what evidence means.
- Forecasts: predictions about what may happen.
- Recommendations: advice about what to do.
A single sentence can contain multiple factual claims. For example, “The policy cut hospital readmissions by 20% in rural clinics” asserts that the policy existed, that the setting was rural clinics, that readmissions were measured, and that the reduction was 20%. Each part may require a different source or qualification. The House of Commons Library’s guidance on working with AI recommends identifying claims precisely and checking them independently.
Do not treat the citation list as a shortcut around this work. The citation is a bibliographic assertion (“this source exists”) and a support assertion (“this source backs this wording”). Both can fail.
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How do I verify an AI citation? A practical workflow
1. Extract and label every checkable claim
Copy the AI output into a working document and split compound sentences into atomic statements. Mark each item as fact, interpretation, forecast, or recommendation. Preserve the model’s exact wording; changing it while checking can hide an overstatement.
For each claim, note terms that determine its meaning: population, location, date, definition, measurement, comparison group, and degree of certainty. “Users improved” is not equivalent to “the average score improved in a randomized trial.”
2. Prioritize by consequence and volatility
Not every sentence deserves the same investigation. Check claims first when an error could affect health, money, legal compliance, safety, public decisions, or someone’s reputation. Also prioritize volatile subjects such as current rules, prices, officeholders, product specifications, and breaking events.
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The University of Nevada, Reno’s AI and source-evaluation guidance recommends matching the depth of checking to likely real-world impact. A low-stakes background detail may need a quick source check; a medical dosage, legal deadline, or investment claim needs the original evidence and independent corroboration.
3. Test whether the reference exists
Search the title and author in more than one appropriate catalogue: a scholarly database, library catalogue, publisher page, government repository, or the organization’s official archive. Confirm all of the following:
- author or issuing organization;
- exact title;
- publication or release date;
- journal, publisher, report series, or repository;
- volume, issue, page range, report number, or dataset identifier when applicable;
- edition, revision, or version;
- the URL or persistent identifier.
Try distinctive title phrases in quotation marks, then search by author and subject if the title produces nothing. A plausible-looking reference that cannot be located is not a verified source. Do not cite it while hoping that another search will eventually find it.
4. Look for composite or altered references
Generative systems sometimes combine real details into a reference that never existed. An author may be paired with another author’s title, a genuine report may be given the wrong year, or a real article may be assigned to the wrong journal. Compare the citation’s fields with the catalogue or publisher record one field at a time. A partial match is not confirmation.
5. Read the original source
Open the source itself rather than relying on an abstract, search snippet, or another page’s summary. Find the passage, table, figure, legal provision, or dataset row on which the claim depends. Ask:
- Does the source make the same assertion?
- Is the AI describing a result, a hypothesis, a limitation, or someone else’s claim?
- Are the population, geography, date, and definitions the same?
- Does the number refer to an absolute count, percentage, rate, relative change, or model estimate?
- Does the source state an association while the AI claims causation?
- Does a limitation, confidence interval, exclusion, or caveat change the conclusion?
A genuine source can be misquoted, mismatched, or used outside its scope. Read enough surrounding context to determine what the authors actually concluded and what they explicitly did not conclude.
6. Record the scope and limitations
Write down the conditions under which the source’s finding is valid. Record the sample or population, geography, timeframe, operational definitions, method, comparison, and known limitations. NIST’s information-quality guidance emphasizes presenting information accurately and completely in context rather than stripping away qualifiers.
Do not turn “in this survey of 412 employees” into “employees generally,” or “the rule in force on a stated date” into a timeless legal fact. If the source is a preprint, preliminary estimate, or older edition, label it that way.
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Leave the original page and search laterally. Look for another reputable source that reached the claim independently, not merely another page repeating the same press release, study, or social-media post. For a statistic, trace it to the original dataset or the organization that produced it when possible. Penn State Libraries recommends checking facts and statistics against at least two reputable sources.
Two articles that copy one another are one evidentiary trail, not two confirmations. Compare publication dates, wording, links, and cited data to detect circular reporting.
8. Choose evidence suited to the claim
Authority is claim-specific. Use the source closest to the underlying evidence:
- official statistical agencies for official statistics;
- primary legislation, court opinions, and regulator notices for legal requirements;
- peer-reviewed studies and their data or protocols for scholarly findings;
- original records, datasets, technical specifications, or experiment logs for measurements;
- the issuing organization’s current documentation for a policy or procedure.
A prominent website is not automatically the best source. NIST distinguishes identifying a source from establishing its accuracy and reliability.
9. Keep an audit trail
Save enough detail for another person to reproduce your check. For every claim, record:
- the AI’s exact wording;
- your narrower, checkable version of the claim;
- source title, author or organization, and URL;
- publication date and version;
- the page, section, table, dataset field, or legal clause examined;
- a short quotation or data transcription;
- your verdict and the reason;
- the date you checked it.
Recheck volatile claims shortly before publication or use. NIST’s information-quality principles treat time sensitivity as part of judging whether a source is reliable for its intended purpose.
Does this source really support the claim?
Use a source-to-claim test instead of asking only whether the citation “looks credible.” A useful record has three separate judgments:
| Question | What a “yes” means | What a “no” means |
|---|---|---|
| Does the source exist? | The bibliographic record, version, and location can be independently confirmed. | Treat the reference as unverified; do not present it as evidence. |
| Is it the source the AI described? | Author, title, date, publisher, and edition match the original record. | Investigate a composite or altered citation. |
| Does it support the exact claim? | The relevant passage or data supports the same proposition, scope, and certainty. | Rewrite, narrow, or remove the claim. |
| Is the evidence current and applicable? | Date, version, geography, population, and definitions fit the intended use. | Qualify the claim or find a current, applicable source. |
Support is not binary in every case. A study may support a limited association but not a causal statement; a legal page may describe an old rule but not the current one. State exactly what the evidence establishes.
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How should you compare multiple sources?
When sources disagree, evaluate them on the same axes rather than counting links.
| Comparison axis | Questions to ask |
|---|---|
| Authority and expertise | Who produced it, what relevant expertise do they have, and what review or accountability applies? |
| Proximity to evidence | Is this the original dataset, legal text, experiment, or official record, or a secondary summary? |
| Independence | Did the source work independently, or does it repeat the same upstream assertion? |
| Date and version | Which source reflects the relevant time, edition, revision, or release? |
| Scope | Do population, geography, timeframe, definitions, and units match? |
| Methods and limitations | How was the result produced, what uncertainty is reported, and what exclusions apply? |
| Source-to-claim fit | Does the source’s actual content support the precise sentence you plan to publish? |
A newer secondary article may be useful for discovery, while an older primary record remains the authoritative evidence for what happened at that time. Explain the apparent conflict instead of selecting whichever headline sounds stronger.
Common AI citation failure modes
Nonexistent reference
The title, author, or publication cannot be found in relevant catalogues, indexes, publisher records, or official repositories. Treat the citation as fabricated or unverified. Do not fill in missing details from guesswork.
Composite or altered reference
Some fields are real but assembled incorrectly: a real author with another work’s title, a genuine report with a changed date, or a valid article assigned to a different journal. Verify every bibliographic field against the primary record.
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The cited work exists but does not contain the claimed finding. The AI may have confused an introduction with results, a cited source with the current paper, or a related topic with the requested one.
Overstated support
A qualified result becomes a universal or certain statement. Common expansions include correlation into causation, a subgroup into the whole population, a short-term result into a permanent effect, or an estimate into an exact count. Restore the source’s limits in your wording.
Circular corroboration
Several pages appear to confirm a claim but all trace back to one press release, study, or unsourced post. Follow links and publication chronology until you identify the independent evidence, if any.
Outdated evidence
A once-accurate rule, statistic, product behavior, or officeholder may no longer be current. Check the publication date, revision history, effective date, and the date on which the claim is meant to apply.
What to do when you cannot find the cited source
- Search the exact title in quotation marks.
- Search the author or organization with distinctive title words.
- Check a scholarly database, library catalogue, publisher archive, and official repository appropriate to the subject.
- Try alternate spellings, report-series names, or an earlier edition without inventing a match.
- Ask the AI to provide a complete bibliographic record and a quotation, then verify both independently. Treat its answer as another lead, not confirmation.
- If the work still cannot be located, mark the citation “not verified,” remove the claim, or replace it with evidence you can inspect.
Do not cite a source merely because a model supplies a DOI-like string, a convincing URL, or a quotation. Automated existence checks can establish that a record or page is present; they do not establish that the content supports the claim.
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How much checking is enough?
Use a consequence-based stopping rule. For a low-impact background statement, locating the source and confirming the wording may be sufficient. For a high-impact claim, continue until you have inspected the primary evidence, checked scope and limitations, and found independent corroboration where available.
- Low consequence: verify the record, read the relevant passage, and preserve the link and date.
- Moderate consequence: add a second reputable source and compare definitions, dates, and methods.
- High consequence: use the closest authoritative or primary evidence, document the exact supporting location, seek independent confirmation, and obtain qualified professional review where the decision requires it.
No workflow can guarantee that a true source is error-free. The aim is a transparent, proportionate basis for the wording you publish or act on.
Scientific integrity and responsible reporting
Fact-checking is part of responsible communication, not merely citation formatting. NIST’s Scientific Integrity Program defines scientific integrity as “the adherence to professional practices, ethical behavior, and the principles of honesty and objectivity when conducting, managing, using the results of, and communicating about science and scientific activities.”
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Provenance or synthetic-content labels can help show where a file or passage came from, but they do not prove that a research claim is true. Evaluate the underlying evidence and the fit between evidence and wording.
A reusable fact-check worksheet
Use one row per atomic claim:
| Field | Entry |
|---|---|
| Exact AI wording | Copy without edits. |
| Claim type | Fact, interpretation, forecast, or recommendation. |
| Risk and volatility | Potential consequence and how quickly it can change. |
| Checkable claim | One narrow proposition with defined scope. |
| Primary or authoritative source | Full bibliographic details, URL, version, and date. |
| Supporting location | Page, section, table, dataset field, or legal clause. |
| Independent source | Separate evidence and why it is independent. |
| Scope and limitations | Population, geography, timeframe, method, uncertainty, and exclusions. |
| Verdict | Supported, partly supported, unsupported, contradicted, or not verified. |
| Checked on | Date and checker’s name or initials. |
Keep the original AI output alongside this worksheet. If the claim changes later, you can see whether the evidence changed or only the wording did.
Writing the corrected result
After checking, choose the narrowest accurate wording. If evidence supports only a limited finding, say so. If sources conflict, identify the disagreement and the reasons—such as different populations, dates, definitions, or methods. If a citation is unavailable, say that the source could not be verified rather than implying that the claim is false or true.
Do not let a prestigious publisher, a long reference list, or a provenance signal substitute for reading the evidence. A careful, reproducible explanation of what is known, unknown, and conditional is more useful than a confident paragraph decorated with citations.
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