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Do not treat a fluent AI answer as evidence. Before using one in a public service or communication, break it into factual claims, verify each material claim against current authoritative sources, check that citations support the exact wording, and have an accountable person approve the final text. The level of review should rise with the consequences of an error. The relevant disclosure, privacy, records, and approval rules depend on your agency and jurisdiction.
How do I fact-check AI-generated information?
Fact-check the claims, not the prose or the AI’s confidence. UK civil-service guidance says to verify reported facts against reliable sources that can be cited, and not to use generative AI as the only source on a topic. The Government of Canada similarly advises federal institutions: “Don’t consider generated content as authoritative.” (UK Government guidance; Government of Canada guide)
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For public-service work, apply this seven-step check before sharing or acting on an AI-generated answer.
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- Define its use and stakes. Identify who will read it and whether it is background, a public-facing explanation, an instruction, or information that could affect rights, eligibility, benefits, health, money, or safety. Higher-impact uses warrant more review and subject expertise. Canada warns that misinformation in public-facing communications and service delivery can cause harm and liability.
- Split the answer into checkable claims. Isolate dates, names, figures, eligibility rules, causal statements, and recommendations. Check each material claim separately. Flag uncertain, time-sensitive, or out-of-scope points for a specialist rather than allowing confident wording to stand in for evidence. UK guidance notes that AI answers can sound convincing, vary across repeated prompts, and draw on sources a user may not otherwise trust.
- Find authoritative, current evidence. Prefer the responsible agency, applicable law or policy, official statistics, a standards body, primary research, or another source with clear authority for the claim. Check jurisdiction and effective date. Do not treat another AI answer—or a source merely repeated by one—as independent confirmation. Canadian federal guidance recommends checking trusted sources or asking a knowledgeable colleague to assess factual and contextual accuracy.
- Open and inspect each cited source. Confirm it exists, is the right version and jurisdiction, and supports the exact wording in its surrounding context. Look for dates, qualifications, and exceptions. A relevant-sounding title is not proof. NIST’s experimental evaluation work offers three useful tests: does the evidence faithfully support the claim, does the summary preserve the source’s full message, and is the evidence sufficient for the claim?
- Review the answer as a whole. Check for missing steps, misleading emphasis, unsupported inferences, bias, privacy exposure, or advice inappropriate to the service context. Verify names, dates, figures, and personal information. CDC guidance calls for review of accuracy, completeness, hallucinations, misleading content, and the validity and appropriateness of citations.
- Document the check and get approval. Keep the final claim wording, source title and URL, publication or effective date, relevant passage or section, reviewer and review date, unresolved caveats, and approval decision. Record AI use and cite the tool and input sources where required by policy. A named person should remain accountable for the released text. UK guidance discusses citing the AI tool and input sources; CMS calls for citations and documentation that support traceability.
- Recheck information that can change. Before reusing content about eligibility, hours, forms, contact details, policy, or regulatory instructions, reopen the primary source. Set a review date for material that changes frequently. UK guidance itself says its advice is subject to review as practice develops.
How can I verify AI answers and their citations?
Use a claim-to-evidence record rather than a list of links pasted beneath the answer. For every material statement, a reviewer should be able to follow the record from the exact wording to the passage that supports it, and see who checked it and when.
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| Review question | What to establish |
|---|---|
| Does the source exist and apply? | Open the original page or document; confirm its authority, jurisdiction, version, and date. |
| Does it support this wording? | Read the relevant passage and surrounding context, including exceptions and qualifications. |
| Is the evidence complete and sufficient? | Check whether the answer omits important parts of the source or makes a broader claim than the evidence supports. |
| Can another reviewer reproduce the check? | Record the source title and URL, relevant passage or section, date, reviewer, and approval decision. |
These checks align with NIST’s experimental work on citation faithfulness, completeness, and evidentiary sufficiency. They are practical evaluation questions, not a universal mandated checklist or a certified product test.
Can I trust AI-generated answers for government services?
Not without verification. An AI answer may help staff draft or gather background, but it should not substitute for the authoritative source or the accountable decision-maker. CDC’s public-health principles state, “Always review GenAI outputs before use.” CMS guidance also calls for oversight before AI outputs are used for business decisions or shared externally.
The review process should reflect the specific risk, rather than assume every use has the same acceptable level of checking:
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- General background: verify material facts and context before publication.
- Service instructions or changing information: check current agency sources, effective dates, and any exceptions; recheck before reuse.
- Rights, eligibility, health, money, or safety: involve a qualified reviewer and the required agency approval process before relying on or publishing the answer.
AI authorship detection is not a substitute for this work. In NIST’s 2025 text-to-text pilot, three generators produced summaries that fooled every detector in the tested set. That finding is limited to the study’s task and tested systems; it does not establish that every detector always fails, and detecting authorship would not prove a claim true or false.
What do adoption statistics say about accuracy?
They do not establish an accuracy rate. OECD’s 2026 Digital Government Outlook reports that 35 of 36 OECD countries (97%) use AI in at least one government area; 30 of 36 (83%) have at least one institution responsible for governing public-sector AI; and 14 of 36 (39%) require pre-deployment risk assessments. These are measures of adoption and governance across countries, not of how often AI-generated public-service answers are correct. The figures also show that governance arrangements differ.
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The guidance cited here comes from different settings: UK civil-service guidance, Canadian federal guidance, U.S. CDC public-health considerations, CMS internal guidance, and NIST evaluation work. They support common practices—checking sources, human review, accountability, and traceable documentation—but do not establish one universal legal standard or error threshold. Follow the applicable local rules for privacy, security, accessibility, records, disclosure, and service delivery.
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