Before an AI Draft Reaches the Public, Check the Claims That Carry Consequences

Use a simple claim ledger to verify facts, protect private information, and keep one accountable person behind every public message.

By Arbab Naseebullah Kasi, Chief Executive Officer - CEO, Feel Worldwide Foundation Inc.

Generative AI can turn rough notes into a polished announcement in seconds. That speed is useful, but polish is not evidence. A confident sentence can still contain the wrong deadline, an invented phone number, a promise your team never approved, or an eligibility rule copied from an outdated source. If a customer, learner, donor, or community member could act on the sentence, someone must be able to stand behind it.

The practical decision is not whether every team should ban AI or trust it. It is where human checking must become nonnegotiable. A small business or nonprofit can begin with one rule: before public release, identify every claim that could change a person's decision, cost them time or money, expose private information, or create an obligation for the organization. Then verify those claims against evidence that exists outside the draft.

Start with consequences, not grammar

Editing for tone, spelling, and clarity is helpful, but it does not reveal whether the underlying details are true. The U.S. National Institute of Standards and Technology calls one generative AI risk “confabulation,” meaning a system can confidently present erroneous or false content. NIST also recommends deploying fact-checking techniques and reviewing sources and citations in generative AI outputs. Read the NIST Generative Artificial Intelligence Profile.

Read the draft once without rewriting it. Mark any sentence containing a date, price, percentage, eligibility rule, address, contact route, promised outcome, legal or safety instruction, quotation, named partner, funding statement, or description of what your organization will do. Also mark comparisons such as “fastest,” “free,” “guaranteed,” “approved,” or “available to everyone.” These are material claims because a reasonable reader may rely on them.

Not every sentence needs the same level of review. “The workshop is designed to support practical learning” is a broad description. “Every participant will receive a recognized certificate and a job interview” creates two specific expectations. Review effort should follow the possible consequence. A typo in a heading may be embarrassing. A wrong application deadline can exclude someone.

A fictional flyer that sounds ready but is not

Consider a fictional community team preparing a flyer for a digital-skills session. A coordinator gives an AI tool a few notes and receives this draft: “Free Saturday training for all adults. Register by October 18. Laptops, transportation, and guaranteed certificates are provided. The venue is fully accessible. Call 555-0199 to reserve your place.”

The language is clear, positive, and specific. Almost every useful detail still needs evidence. Did the approved budget remove every fee? Is the deadline current? Are laptops available for each participant or shared in pairs? Has transportation actually been arranged? Who issues the certificate, and what must a learner complete? Was accessibility checked with the venue rather than inferred from a photograph? Does the phone number belong to the responsible team?

The reviewer should not ask the AI to “double-check” its own answer. That may produce a second confident draft without independent evidence. Instead, the team returns to the approved event plan, current registration page, venue information, named staff owner, and any written partner commitment. Where the evidence is missing, the claim is not ready.

Suppose the records confirm a limited number of loaner laptops, step-free entrance to the main room, and a participation note issued only after completing the session. The corrected flyer can be both more accurate and more respectful: “A limited number of loaner laptops can be requested during registration. The main training room has step-free access; contact the organizer about other access needs. Participants who complete the session may request a participation note.” Precision is not weaker writing. It gives readers a fair basis for planning.

Build a claim ledger, not a vague feeling of confidence

For one important draft, make a four-column claim ledger. In the first column, copy the exact claim. In the second, link the current source or name the person with direct responsibility for that fact. In the third, record one decision: keep, rewrite, remove, or escalate. In the fourth, name the human reviewer and review date.

Illustration of fictional colleagues checking a public-information draft against its sources.

A useful entry might say: “Transportation is provided | no signed arrangement found | remove | reviewed by program lead, October 6.” Another might say: “Registration closes October 18 | current registration page and event owner agree | keep | reviewed by communications lead, October 6.” If the source is a webpage, open the page rather than trusting a search-result snippet. If the claim comes from a person, ask whether they are authorized to commit the organization.

This ledger creates a small evidence trail. It also separates two different questions: “Did AI write this sentence?” and “Can our organization support this sentence?” The second question matters more. Human-written drafts can be wrong, and AI-assisted drafts can be accurate after disciplined review.

The Federal Trade Commission has applied familiar evidence expectations to claims involving AI. In an April 2025 proposed order, the FTC alleged that an AI detection company promoted an accuracy figure that was not supported for general-purpose content and required competent and reliable evidence for future effectiveness claims. The case concerns a particular company and is not a rule for every communication, but it illustrates a wider business discipline: do not publish a precise performance claim because it sounds credible. Read the FTC announcement and proposed-order summary.

Keep private information out of the drafting process

Verification is only one part of responsible use. A team may expose information before the first sentence is generated. Do not paste a participant list, donor record, employee issue, private application, unpublished financial information, or identifiable case notes into an AI tool merely to make drafting easier. Replace real details with the minimum context needed, use fictional placeholders, and follow the organization's approved privacy and security procedures.

NIST's Generative AI Profile notes that models can leak, generate, or infer sensitive information and recommends monitoring AI-generated content for possible personal or sensitive data exposure. NIST's Privacy Framework is a voluntary tool for identifying and managing privacy risk while protecting individuals. These resources do not replace applicable law or professional advice, but they support a sound operational question: what information does this task genuinely require? Explore the NIST Privacy Framework.

Run a separate privacy pass before release. Search the draft for names, phone numbers, email addresses, addresses, account details, health information, student records, employment matters, or descriptions that could identify a person indirectly. Confirm that each item is necessary, approved, and intended for publication. Removing a name from the prompt after it has already been shared does not undo the original disclosure.

Make the reviewer responsible for a decision

“A human reviewed it” is too vague to protect a reader. Assign review according to the claim. A communications editor can improve clarity. A program owner should confirm dates, capacity, eligibility, and what the team will deliver. A finance owner should verify prices or funding statements. A safeguarding, privacy, legal, or technical specialist may be needed when the consequences justify it.

NIST's guidance includes documenting human oversight roles, sharing pre-deployment testing results with people who have release authority, and verifying sources and citations. A small team does not need a complicated committee, but it does need one identifiable person with authority to say yes, no, or not yet. The approver should see the evidence, not only the clean final draft.

Create a clear escalation rule. If a claim concerns safety, legal rights, medical or financial guidance, a vulnerable person's situation, a public funding commitment, or a partner's reputation, pause ordinary publication and send it to the appropriate qualified owner. AI should not turn a missing expert decision into a fluent substitute.

After publication, keep the ledger with the final version and source-check date. Set a review date for information that changes, especially deadlines, fees, addresses, rules, and contact routes. If an error is found, correct the public text, note the change when readers could have relied on the earlier version, and improve the checking step that failed.

This approach connects responsible technology use with practical learning. FWF's Education, Youth Development & Digital Access pathway emphasizes learning that builds capability and confidence. Choose one AI-assisted draft your team expects to publish. Mark the five claims with the greatest consequences, build the four-column ledger, and do not release the message until each claim has an evidence status and a named human owner. Organizations can connect with FWF to discuss education and partnership interests without sending private participant records.

Sources reviewed October 6, 2026. The flyer and claim-ledger entries are fictional teaching examples. This article offers general educational guidance, not legal, privacy, or professional advice, and it does not state that FWF provides AI-review services.