When AI helps produce research that informs a decision, reviewers tend to ask the same seven questions: what sources it used, whether the result can be reproduced, how accuracy was checked, who approved it, where the data went, how conflicts and gaps were handled, and what changed since the last time. If your team can answer all seven with evidence, AI-assisted work is far easier to defend. This guide explains each question, what a good answer looks like, and what to keep on file.
Why Are Reviewers Asking More Questions About AI Research?
AI makes it faster to produce research, and it also makes it easier to produce research that nobody can check. A reviewer, auditor or board member cannot tell from the output alone whether it rests on solid sources or on a confident guess. The questions below are how they find out.
A useful reference is the NIST AI Risk Management Framework. Released on January 26, 2023, it describes seven characteristics of trustworthy AI: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed, according to a Hunton Andrews Kurth summary. The framework is voluntary, but several of the questions below map directly to it.
The 7 Questions
1. What sources did the AI use?
What a good answer looks like: a list of the specific sources behind each finding, with links a reviewer can open and the date each was accessed. Findings without a source are treated as unverified.
Evidence to keep: source links, access dates and, for important documents, a saved copy.
2. Can the result be reproduced?
What a good answer looks like: a record of what was asked, which tool and version produced the answer, and when. AI output can change from one run to the next, so the record matters more than the memory of it.
Evidence to keep: the prompt or task description, the tool name and version, the date, and the output as it was delivered.
3. How do you know it is accurate?
What a good answer looks like: a description of how the output was checked, such as spot checks against original documents, comparison with a known case, or review by a subject expert. This links to the NIST characteristic of being valid and reliable: results should be confirmable with objective evidence.
Evidence to keep: a checking log with what was tested, what was found and what was corrected.
4. Who reviewed and approved it?
What a good answer looks like: a named person who reviewed the work and accepted responsibility for it. This is the accountability question, and “the AI said so” is not an answer.
Evidence to keep: reviewer name, date and any changes they made or requested.
5. What data did you give it, and where did it go?
What a good answer looks like: a clear account of what information was provided, who could access it, where it was stored, and whether it could be used to train models. Sensitive information deserves extra care.
Evidence to keep: the provider’s security and privacy documentation, your data-handling policy, and the contract terms that apply.
6. How did you handle conflicting or missing information?
What a good answer looks like: a record of where sources disagreed, which one was used and why, and which questions remain unanswered. Admitting uncertainty is a sign of a careful process.
Evidence to keep: a list of conflicts, how each was resolved, and open items.
7. What changed since last time?
What a good answer looks like: a change log that shows what was new, what was updated, and why a conclusion was revised. This matters most for ongoing work such as monitoring and periodic reviews.
Evidence to keep: dated versions of the output and a short note on each change.
How Do the Questions Map to Trustworthy AI?
| Question | Related NIST characteristic |
| 1. Sources | Accountable and transparent |
| 2. Reproducibility | Valid and reliable |
| 3. Accuracy | Valid and reliable |
| 4. Approval | Accountable and transparent |
| 5. Data handling | Privacy-enhanced; secure and resilient |
| 6. Conflicts and gaps | Explainable and interpretable |
| 7. Change over time | Valid and reliable; accountable and transparent |
What Does a Simple Documentation Pack Include?
You do not need a heavy process to answer all seven. A short file kept with each piece of AI-assisted research is enough for most uses: the task description, the sources list, the tool and date, the checking log, the reviewer’s sign-off, the list of conflicts and open items, and a change note if the work was updated. Keep it in the same place as the output so nobody has to reconstruct it later.
Can Tools Help You Answer These Questions?
Some of the work can be built into the tool itself. Look for products that show a source for each finding, keep a record of what was done, and make it easy for a reviewer to approve or correct the output. Grep, built by Parcha Labs, designs its custom AI agents around this standard, producing citation-backed reports, slide decks and spreadsheets that are meant to be traceable, auditable and defensible to a board or regulator, and it says it has been in regulated production since 2023. Whatever you use, test it against the seven questions before you rely on it.
Final Thoughts
The seven questions come down to one test: can you show your work? Keep the sources, record the process, name the reviewer, and note what you do not know. With that in place, AI becomes a faster way to do research that still stands up to review.
Frequently Asked Questions (FAQs)
What is an AI research audit?
It is a review of how AI-assisted research was produced, covering its sources, accuracy checks, approvals, data handling and documentation.
Do regulators require AI audit trails?
Requirements vary by sector and jurisdiction. Existing expectations for documented, risk-based decision-making still apply when AI is involved, so check the rules that apply to you.
What should an AI audit trail include?
The task, the sources, the tool and date, the checks performed, the reviewer, conflicts and open items, and a change log.
Can AI-generated research be relied on?
It can be useful as a starting point and a time saver, but it should be checked and approved by a person, especially for material decisions.
How often should AI-assisted research be reviewed?
It depends on the risk. Ongoing work such as monitoring should be reviewed on a schedule, and any output used for a major decision should be reviewed before it is relied on.
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