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What a useful disclosure should say
A useful disclosure lets the reader understand the scope of AI’s involvement and the human work behind the result. Avoid a vague statement such as “AI was used” when you can identify the task and your review.
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- Work affected: Name the document, analysis, code, presentation, or other output—or the relevant part of it.
- AI’s role: Say what the tool did, such as organizing notes, suggesting formulas, drafting text, or generating options.
- Tool details: Name the tool and, if known and relevant, its model or version.
- Purpose: Explain why you used it, such as to create a first draft or explore alternatives.
- Your contribution and oversight: Describe the instructions, decisions, checks, revisions, tests, and approval you actually provided.
The CDC’s guidance for scientific work groups disclosure around “Content Affected + Action Taken + AI Tool + Purpose of AI Use + Human Oversight.” That is a helpful structure for workplace disclosures too, but it is not a universal employee rule or a substitute for your organization’s policy.
A practical formula you can adapt
Use this as a starting point, removing any clause that is not true:
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“I used [tool or model, if known] to [specific task] for [purpose]. I [describe your actual contribution, such as selecting inputs, directing the draft, checking facts, editing, testing, or making the final decision]. I reviewed the result and am responsible for the version I’m sharing.”
For example: “Portions of the introduction and discussion were edited using [AI tool and model, if available] for language refinement. The authors reviewed and approved the edits.” The CDC gives this kind of statement as an example for scientific writing; adapt it to your situation rather than treating its wording as a standard for every workplace.
How to explain what you contributed
Describe your work in concrete verbs, not in a broad claim that you “oversaw” the AI. A useful account separates the tool’s task from the human decisions and checks that shaped the final result.
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- Framed the task: Set the goal, constraints, audience, or criteria.
- Selected the inputs: Chose the source material or information to provide, while following workplace data rules.
- Directed and evaluated: Wrote instructions, compared suggestions, rejected unsuitable output, or chose among alternatives.
- Checked or tested: Verified facts against reliable sources, tested formulas or code, or checked names, figures, and decisions against original records.
- Revised and approved: Changed the output, applied professional judgment, and decided whether it was fit to share or use.
Include only actions you really took. If you did not verify a claim or test a formula, do not imply that you did. NIST’s AI Use Taxonomy offers shared terms for describing human-AI activities and outcomes; it does not prescribe a workplace credit formula.
Examples for common work tasks
These are illustrative examples, not quotations from the cited guidance. Adapt them to the facts and your organization’s rules.
Meeting notes
“I used an AI assistant to organize the meeting notes into a first draft. I checked names and decisions against my notes, rewrote the action items, and approved the version sent to the team.”
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“I used AI to brainstorm options. I chose the recommendation after checking it against the project requirements.”
Drafting or editing
“I used [tool] to draft [specific section] for [purpose]. I checked the claims against [the materials I used], revised the language, and approved the final version.” Do not say you checked claims against materials unless you did so.
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Choose the amount of detail for the audience and stakes
A brief note to a teammate may be enough for low-stakes internal work if policy permits it. A client deliverable, public document, or output that informs a consequential decision may call for a more explicit account. Before deciding how much to say, consider:
- Who will rely on it: A colleague, client, public audience, or decision-maker?
- What rules apply: Does your employer, client, funder, regulator, or publication require a particular disclosure?
- How much AI contributed: Was it used to brainstorm, refine wording, draft a substantial portion, or analyze information?
- What review occurred: Did you edit, validate, test, or independently check the output—and how?
- What information was involved: Was the material sensitive, confidential, proprietary, or otherwise restricted?
If someone asks about your use, be ready to describe the scope and the checks you performed. Do not claim that the model made a judgment you made, or describe AI-generated material as entirely unaided human work.
Check policy and protect work information before using AI
Workplace expectations differ by organization, use case, and jurisdiction. Check your employer’s or client’s policy for approved tools, disclosure requirements, and rules on handling data before entering work material or sharing an AI-assisted result. When a policy is unclear, ask the relevant manager, privacy, security, legal, or compliance contact rather than assuming that a public tool is approved.
The U.S. Consumer Product Safety Commission’s internal policy illustrates why the tool and data rules matter: it tells personnel to critically review and edit AI output before formal use and cautions against sharing confidential or proprietary information with public AI systems. That is an agency policy, not a rule for every employer. Follow the requirements that apply to your workplace.
Is disclosure legally required?
There is no universal rule in the sources cited here requiring every employee to disclose every AI interaction. Requirements depend on the organization, use case, and jurisdiction. The OECD’s 2024 report describes an uneven policy landscape and discusses particular workplace contexts; it does not establish a general disclosure mandate for all workers.
For example, New Jersey guidance requires prominent disclosure for specified state systems that interact with residents or affect decisions. That requirement is limited to its stated systems and context, not a blanket rule for private-sector employees. For a legal question about a particular workplace or use, consult the applicable policy or a qualified adviser.
Why a clear account matters
AI use may not be obvious to someone reading the result. The OECD report cites a 2023 study finding that 17% of adults in the United Kingdom could often or always tell when they were using AI. That figure concerns the stated UK adult population and measure; it is not a statistic about workers generally. A specific disclosure helps a reader understand the tool’s role and the human review behind the work without overstating either.
Microsoft’s responsible-AI guidance recommends review and editing, reminders that AI output may be inaccurate, and making clear that people remain accountable for final content. These are vendor recommendations, not employment law. The practical point for an employee is to review output before formal use and describe that review accurately.
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