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Do not treat an AI-generated report as evidence until a human audit verifies it. Define the decision the report will support, identify the cost of a wrong call, and break the report into material claims. Then open and check every important citation, trace data and methods, independently reproduce consequential calculations, test whether conclusions remain stable under reasonable variations, and obtain qualified subject-matter review for high-risk findings. Record the report version, model, inputs, tests, exceptions and approval decision so someone else can reconstruct what happened.
What should you decide before checking an AI-generated report?
Start with the decision, not the prose. A report used to choose a marketing slogan can tolerate a different error rate from one used to approve a loan, diagnose a condition, set a safety limit or assess legal exposure. Write down the decision, the people affected, the deadline, the acceptable uncertainty and what happens if the report is wrong.
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Classify the consequence of error
- Low consequence: an error is inconvenient but easily reversible. A lighter review may be sufficient, provided claims are still checked.
- Material consequence: an error changes spending, staffing, eligibility, compliance or a public statement. Require source verification and independent calculation.
- High consequence: an error could affect health, safety, finances, legal rights, employment, public policy or security. Escalate to a qualified reviewer with authority to reject the report.
Also identify what “good enough” means. A useful acceptance rule might require every material claim to have an accessible, current source; every decision-changing number to be reproducible; and every unresolved limitation to be accepted by a named decision owner. This prevents a polished narrative from becoming the default approval.
How do you turn a long report into auditable claims?
Read once for context, then make a claim register. Copy each statement that could change the decision, including figures in charts, footnotes and executive summaries. Do not audit only the headline conclusion: a small unsupported premise can invalidate an otherwise reasonable recommendation.
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Use a claim-review table
| Field | What to record |
|---|---|
| Claim | The exact statement, number or recommendation being tested. |
| Source | Original document, dataset, interview or system record; include a stable title or identifier. |
| Date and version | Publication date, data period, model/report version and retrieval date. |
| Jurisdiction and scope | Country, population, business unit, product edition or other boundary that limits applicability. |
| Confidence | Reviewer’s current confidence and the reason for it, not the model’s self-rated confidence. |
| Test performed | Source match, recalculation, sensitivity test, consistency check or expert assessment. |
| Reviewer | Name, role and relevant subject-matter qualification. |
| Disposition | Accepted, corrected, qualified, rejected or escalated, with a short rationale. |
Preserve the report’s exact wording before editing it. If you correct a claim, keep the original, the replacement and the evidence that changed your view. This creates an audit trail and makes later model or source changes visible.
How do you check for fabricated citations and unsupported claims?
Generative systems can present false statements confidently and can invent citations, passages or reasoning. NIST calls this failure mode “confabulation.” A citation is not evidence until a reviewer opens the original source and matches it to the precise claim.
Verify each material citation
- Open the cited publication, dataset, regulation or record rather than relying on the report’s quotation.
- Confirm that the source exists and that its title, author, publisher and date are correct.
- Locate the exact passage, table or calculation the report appears to use.
- Check whether the passage supports the claim as written, including qualifiers such as “may,” “in this sample” or “under these conditions.”
- Record the source version, geography, definitions, time period and scope limits in the claim register.
- Mark the claim unsupported if the source is inaccessible, unrelated, misquoted or too weak for the decision.
Watch for citations that point to a real document but attribute a result it never reports. A real source can still be misapplied: a study of one population does not automatically establish a result for another, and a policy draft is not the same as an enacted rule.
Separate source-backed facts from model synthesis
Label whether each sentence is directly supported, a calculation from supplied data, an interpretation or a recommendation. Interpretations can be useful, but they require a reviewer to test the reasoning and disclose the assumptions connecting evidence to the conclusion. If the report provides no provenance for a key input, treat the input as unverified rather than filling the gap with confidence language.
How do you test evidence, provenance and fitness for purpose?
The evidence must be suitable for the decision, not merely related to the topic. The National Telecommunications and Information Administration says, “AI audits should, at a minimum, be able to evaluate claims made about an AI system’s fitness for purpose, performance, processes, and controls.” Apply those four questions to the report and to the system that produced it.
Assess fitness for purpose
- Does the data measure the outcome the decision actually concerns, or only a convenient proxy?
- Does the population, time period and geography match the intended use?
- Are important cases missing, such as non-responders, failed transactions or people excluded from collection?
- Is the precision appropriate for the decision, or does a rough estimate appear overly exact?
Trace provenance and governance
Record where each dataset came from, who could alter it, when it was retrieved and what transformations occurred. Look for access controls, retention rules, quality checks and documented ownership. A report that cannot identify its source files or retrieval dates cannot be reliably reproduced.
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Inspect disclosed methods and limitations
The report should state the model or system used, prompts or instructions that materially shaped the output, retrieval method, inclusion and exclusion rules, known data gaps and evaluation method. Missing limitations are themselves a review finding. Do not infer that an omission means the process was sound.
How do you independently reproduce calculations?
Every number that could change the decision deserves an independent path from input to result. Obtain the underlying data, rerun the arithmetic or code outside the generating system, and compare your result with the report. Reproduction should use documented units, denominators, rounding rules and date ranges.
Check the calculation chain
- Copy the raw inputs and preserve their original units and precision.
- Recreate intermediate values, not just the final total.
- Verify denominators, weights, time windows, currency conversions and population definitions.
- Compare tables, charts, narrative and conclusion for internal contradictions.
- Run reasonable alternative assumptions, such as a different growth rate, missing-data treatment or cutoff date.
- Record the formula, software or spreadsheet version, reviewer and any difference from the reported result.
AUASB guidance warns against over-reliance on AI-generated information and calls for review of inputs, assumptions, logic, outputs and evidence authenticity. A calculation that cannot be reproduced because inputs are missing, proprietary or changed after generation should be marked as non-reproducible and excluded from high-consequence decisions unless an authorized expert accepts that limitation.
Use sensitivity tests to expose fragile conclusions
Change one material assumption at a time, then test a combined reasonable scenario. Identify the point at which the recommendation changes. A conclusion that flips under a small, plausible change should be presented as conditional, not as a settled finding.
How do you test groundedness and consistency?
Groundedness asks whether generated claims can be traced to reliable source material and whether citations are accurate and relevant. AWS recommends measuring this explicitly. Groundedness is different from fluent writing: a well-written paragraph can still contain unsupported leaps.
Run groundedness checks
- For each material sentence, identify the supporting passage or mark it as synthesis.
- Check that the cited passage is relevant to the exact claim, not merely to the general subject.
- Look for numbers, names and causal statements that appear in the report but nowhere in the sources.
- Check whether the report silently combines findings from incompatible definitions or populations.
Test stability across prompts and versions
Ask the system the same question in several neutral phrasings and regenerate only the sections that drive the decision. Compare claims, numbers, citations and confidence language. Large unexplained changes indicate instability and should trigger manual investigation rather than averaging the answers. Preserve each prompt, retrieved source set and output so the comparison is repeatable.
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Automated checks can screen for broken links, arithmetic errors, duplicate sources and citation mismatches, but they cannot reliably judge subtle context. A domain expert must review consequential claims, especially where terminology, exceptions or professional standards matter.
When is qualified human review mandatory?
Escalate instead of approving when the report affects health, safety, finances, legal rights, employment, public policy or security; when evidence is weak, inaccessible or contradictory; when the model or source data is unknown; or when reviewers find unexplained discrepancies. FRC guidance on generative and agentic AI in audit, published March 30, 2026, emphasizes audit-quality risks, mitigation and professional judgment.
Match the reviewer to the risk
- Subject-matter expert: checks definitions, context, causal reasoning and whether the recommendation is professionally plausible.
- Data or quantitative reviewer: checks sampling, transformations, formulas, uncertainty and sensitivity analysis.
- Governance, legal or privacy reviewer: checks authority, permissions, confidentiality, fairness and regulatory obligations.
- Decision owner: accepts residual uncertainty, sets conditions and has explicit authority to override or reject the AI output.
Independence matters. The person who commissioned or tuned the report should not be the only person deciding whether it is reliable, particularly when the result supports a predetermined outcome.
Which trustworthiness criteria should an audit cover?
Use the National Institute of Standards and Technology AI Risk Management Framework (AI RMF) 1.0 as a checklist of review dimensions. Published January 26, 2023, the framework is voluntary and use-case agnostic; more than 240 organizations contributed to its development. It is a structure for asking disciplined questions, not a certification that a report is correct.
| Criterion | Questions for the report review |
|---|---|
| Validity and reliability | Does the method measure what it claims, and does it perform consistently on relevant data? |
| Safety | Could an error create physical or operational harm, and are safeguards and stop conditions defined? |
| Security and resilience | Could prompts, files or retrieval channels be manipulated, and can the process recover from bad inputs or outages? |
| Accountability and transparency | Are ownership, provenance, limitations, review actions and approval authority recorded? |
| Explainability and interpretability | Can a reviewer understand how inputs led to the result well enough to challenge it? |
| Privacy enhancement | Were personal or confidential data minimized, protected and handled under applicable rules? |
| Fairness with harmful bias managed | Could data, definitions or errors disadvantage a group, and were relevant subgroup results examined? |
Apply the criteria proportionately. A short internal summary may not need the same testing depth as a report that controls access to a service, but no criterion should be silently assumed to be satisfied.
How do you document approval, rejection and later review?
Keep a complete evidence package with the report and its claim register. At minimum, retain:
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- the exact report file, generation date, model or system identifier and version;
- prompts, system instructions, source files, retrieval dates and data transformations;
- citation checks, independent calculations, sensitivity results and consistency comparisons;
- reviewer names, qualifications, conflicts and unresolved exceptions;
- the final approval, conditional approval or rejection, including who made it and why;
- the override path and the conditions that require reopening the decision.
Set a review trigger for source updates, model changes, new data, changed law or a material incident. A report can be accurate when issued and become stale later; record the next review date or event rather than treating approval as permanent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does a practical audit workflow look like?
The following sequence works for a single report or as the basis of an organizational control.
- Define the decision and risk. Name the decision owner, affected parties, error consequence and acceptance criteria.
- Freeze the artifact. Save the report, model identifier, prompts, source set and retrieval date.
- Extract material claims. Enter claims, numbers and recommendations in the review table.
- Verify provenance. Open every important source and record date, version, jurisdiction, definitions and scope.
- Reproduce the numbers. Recalculate independently, reconcile intermediate steps and test alternative assumptions.
- Test groundedness and stability. Match claims to passages, rerun key questions with varied wording and investigate contradictions.
- Conduct risk-based expert review. Escalate consequential or disputed findings to qualified reviewers.
- Decide and document. Accept, correct, qualify, reject or escalate each claim; then record the overall decision and override authority.
- Schedule reassessment. Reopen the report after specified data, source, model or policy changes.
What common audit failures should you avoid?
Approving because the prose sounds certain
Confidence is a style characteristic, not evidence. Require a source, calculation or clearly labeled interpretation for every material statement.
Checking links without checking meaning
A working URL proves only that a document exists. Read the cited passage and confirm its definitions, date, population and qualifiers.
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An unchanged total can hide a wrong denominator, unit conversion or omitted category. Recreate intermediate steps and inspect the underlying data.
Using a second AI output as the deciding reviewer
Another model can help find candidates for checking, but it may repeat the same unsupported premise or introduce a new one. Human review remains necessary for consequential claims.
Ignoring inaccessible evidence
If a source cannot be opened or the underlying data cannot be obtained, mark the claim’s evidence limit and adjust the decision or obtain an independent source. Do not convert an access problem into assumed validity.
Failing to record exceptions
Unresolved discrepancies often disappear from the final version. Keep them in the approval record, assign an owner and state whether the decision is conditional on resolving them.
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How should you choose the depth of review?
Choose a method by consequence and evidence quality rather than by report length. A short report with one high-impact claim can need more work than a long descriptive summary.
| Review approach | Appropriate when | Minimum controls |
|---|---|---|
| Screening review | Low-consequence, reversible decisions with accessible sources. | Claim extraction, citation spot checks, basic arithmetic and documented reviewer. |
| Standard substantive review | Material operational, financial or compliance decisions. | Full citation matching, independent calculations, provenance review, sensitivity tests and consistency checks. |
| Enhanced assurance review | Health, safety, legal rights, employment, public policy or security decisions, or any report with weak evidence. | Qualified independent experts, full reproducibility package, subgroup and fairness analysis, privacy and security review, explicit approval or rejection authority. |
Reassess the level if a screening review uncovers fabricated citations, unstable outputs, unexplained discrepancies or missing source data. Those findings are signals that the report’s apparent simplicity is misleading.
Frequently Asked Questions
Can I use another AI system to fact-check the report?
You can use a second system to flag possible errors, extract citations or suggest alternative calculations, but its output is another unverified artifact. Treat it as triage and have a qualified human confirm any issue that could change the decision.
What if the report cites a paywalled or confidential source?
Ask the source owner for a reviewable copy or an independently verifiable excerpt, subject to access rights. If no reviewer can inspect the evidence, disclose that limitation and do not assign the claim the same confidence as an open, checkable source.
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Minimize personal and proprietary information in prompts and working files, restrict access to authorized reviewers, and record what was shared with the generating system. Privacy controls are part of report reliability because an improper disclosure can make an otherwise correct process unacceptable.
When should an approved report be reopened?
Reopen it when a source, dataset, model, prompt, legal requirement or material assumption changes, or when an incident reveals an error. Keep the original decision and create a new version so later reviewers can see what changed and why.
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