A structural map of disclosure obligations, a taxonomy of seven kinds of use that makes the question decidable, and the list of outputs that must never ship at all.
We have not measured this one. It is the file in this category we would most want people to read anyway.
What it carries is a map of where disclosure is actually required rather than merely polite, kept separate from opinion. The academic branch, where the settled position across the major bodies and publishers is that a generative tool cannot be listed as an author, because authorship requires taking responsibility and a tool cannot, and where use is described in the methods or acknowledgements instead, often with the specific tool and version named. The advertising branch, where the issue is not the writing at all but a material connection or a synthetic testimonial. Platform labelling for realistic synthetic media. Institutional policy in employment and education, which is the governing rule in those settings and beats every general principle. And statutory transparency obligations, of which the European Union's AI Act is the most developed example, with provisions phasing in on a timetable that has itself been subject to revision.
The part that makes the question decidable is a taxonomy of seven kinds of use: research assistance, drafting, editing a human draft, translation, summarising, image generation, and data generation. These carry genuinely different obligations, and lumping them together as "used AI" is the reason people cannot answer whether they need to disclose. The file also sets a quality bar: a real disclosure names the tool, the part of the work, and who checked it. A footnote saying artificial intelligence was used somewhere is not a disclosure.
Who this is not for. It is not for anyone who wants a yes or no answer without reading their own policy, because the honest answer is nearly always a pointer to a document you have to open. It is not legal advice and does not pretend to be. And if your question is how to make a draft pass a detector, this file argues against you at length.
Measuring one skill honestly costs about twenty model sessions: five runs with it, five without, on real material, each output graded alone by a session that is not told the other arm exists, against a rubric written by somebody who never saw the skill. We have not spent that on this one yet, so it ships labelled rather than ships silently.
How it would be measured. Tier A spine available. Assemble twenty real scenarios whose governing policy is known, drawn from published journal author instructions, platform help pages and regulator guidance, all re-checked on the day of the test, and score whether the skill routes each to the correct policy family and the correct disclosure form, counting both misses and over-disclosure. Tier B pairwise review by an editor for the wording of the disclosures it drafts.
The objective spine here is real but perishable. You could assemble twenty scenarios whose governing policy is known, drawn from journal author instructions, platform help pages and regulator guidance, and score whether the routing is correct in both directions, counting over-disclosure as a miss as well. The problem is that the answer key ages. A policy set checked in one month can be wrong the next, so the material has to be re-verified on the day the test runs, which makes the whole thing expensive to repeat and impossible to publish as a standing result. A skill whose value depends on policy currency is also the kind that should be re-read rather than trusted, which is a slightly awkward thing for a directory to publish, and we would rather say so than not.
The rule that decides pass or fail was written down before any run was executed and it does not move afterwards. It is in the method note on the hub, along with the full results table including every skill that was tested and cut.
Stated plainly, because a skill that claims everything is useful for nothing.
name and description in the file's frontmatter, so you can also invoke it by name.For the academic branch there is no substitute for the primary sources, and both are free. Read your target journal's author instructions first, then the bodies listed above. Any third-party summary, this one included, is at best a map of where to look.
For the commercial branch, the regulator that governs your market publishes guidance on endorsements, testimonials and reviews, and it is written for non-lawyers. Read it directly. For anything with real exposure, a lawyer in your jurisdiction is the answer and an hour of their time is cheap next to the alternative.
Where this file earns its place is the middle case: you are not sure which of those branches you are even in, the use is a mixture of drafting and editing and research, and you need a taxonomy to make the question answerable before you go and read the right policy. That is a narrow job, and it is the one it does.
--- name: ai-disclosure-audit description: Decides whether a piece of work needs to disclose that a generative model was involved, and what a real disclosure has to say. Maps the settings where disclosure is required rather than optional, including academic publishing, advertising and endorsement rules, platform policies for synthetic media, institutional policy in employment and education, and statutory transparency obligations. Carries a taxonomy of seven kinds of use that carry different obligations, and a list of outputs that must never be produced at all. This skill should be used before publishing, submitting or shipping any work a model helped produce. --- # AI disclosure audit ## The claim this skill is built on "Should I disclose that I used AI?" is not one question. It is at least five, they have different answers, and the reason people find it unanswerable is that they are trying to answer it about the tool rather than about the use and the setting. Two things make it decidable. The first is knowing which policy family you are in, because the obligation comes from a publisher, a regulator, a platform or an institution, and never from a general principle. The second is being precise about what the model actually did, because using a model to find papers and using it to generate the data in a figure are not the same act and no policy treats them as the same act. **Everything in this file is a structural map, not legal advice.** Obligations differ by jurisdiction, by publisher, by platform and by employer, and this area has been changing quickly for several years. Every policy detail below must be re-checked against its source at the point of publication. Where a rule matters to you, read the actual document, not this summary of it. ## The map: where disclosure is required rather than polite ### Academic and scholarly publishing The most settled branch, and the one with the clearest consensus. **Authorship.** The major journal bodies and the large publishers have converged on the position that a generative tool cannot be listed as an author. The reasoning is consistent across them: authorship carries accountability for the work, including the ability to approve the final version and to answer for its integrity, and a tool cannot hold any of that. This position was set out by the Committee on Publication Ethics and by the International Committee of Medical Journal Editors in 2023, and adopted by the large commercial publishers over the same period. Check the current wording at the source, because the surrounding requirements have been revised since. **Description of use.** Where a tool was used, journals generally require it to be described, in the methods if it touched the research and in the acknowledgements if it touched only the writing. Many require the **specific tool and version** to be named, and some ask for the dates of use and the purpose. Some also require a statement that the authors reviewed and take responsibility for the output. **Images and data.** Several publishers apply stricter rules to generated images than to generated text, in some cases prohibiting them outright outside of papers about the technology itself. If your submission contains a generated figure, this is the specific thing to check, because a policy that permits drafting assistance may still refuse the figure. **Peer review.** A separate and often stricter rule. Several publishers and funders prohibit putting a manuscript under review into an external tool at all, on confidentiality grounds, which is a question about permission rather than disclosure. ### Advertising, endorsements and reviews Here the issue is usually not that a model wrote the copy. It is whether the content misrepresents a relationship or an experience. - **Material connections** between an endorser and a brand must be disclosed under the endorsement rules of most advertising regulators, and this is true whoever or whatever wrote the words. - **Synthetic testimonials and reviews** are the live risk. A review attributed to a customer who does not exist is a false review, and consumer protection regulators have moved specifically against fake reviews and testimonials in recent years, including a dedicated rule finalised in the United States in 2024 and provisions in United Kingdom consumer law from the same period. Check the current rule in your market. The safe reading is simple: do not generate a review, a testimonial, or a quoted customer experience. - **Generated depictions of people** in advertising bring in both the endorsement rules and, in some jurisdictions, publicity and likeness rights. ### Platform policies for synthetic media The largest video and social platforms introduced disclosure or labelling requirements for realistic synthetic media during 2023 and 2024, generally aimed at content that could mislead a viewer into thinking a real event happened or a real person said something. Several also apply automatic labels using embedded provenance metadata, and the open standard most of them reference is Content Credentials from the C2PA. Requirements differ per platform and change, so check the current help page for the platform you are posting to. Political advertising is usually a separate and stricter category. ### Employment and education **The governing rule is the institution's own policy, and it overrides every general principle in this file.** A university's academic integrity policy, an employer's acceptable use policy, a client's contract terms. If a policy exists, it is the answer. If none exists, ask in writing, which also creates the record that protects you. Adjacent obligations exist that are not really about disclosure at all: several jurisdictions regulate automated tools in hiring, including a New York City law enforced from 2023 that requires bias auditing and candidate notice for automated employment decision tools. If a model is in a decision path about people, that is a different and heavier regime. ### Statutory transparency obligations A growing set of laws require that content generated or manipulated by these systems be marked or disclosed. The most developed example is the **European Union's AI Act**, which entered into force in August 2024 and applies on a phased timetable. Its transparency provisions cover systems that interact with people and content that is artificially generated or manipulated, including a machine-readable marking duty and a disclosure duty for deepfakes, with exceptions including artistic and satirical work. The timetable for the transparency tier has itself been the subject of revision and implementation guidance, so **check the current position rather than relying on any date quoted here**, including the dates in this paragraph. Other jurisdictions have their own: China has had rules on deep synthesis services since 2023 and introduced a labelling measure for synthetically generated content afterwards, and several United States states have enacted disclosure rules for synthetic media in election advertising. **One structural point that resolves a lot of confusion.** Some of these obligations fall on the provider of the system rather than on the person using it. A law requiring a large model provider to embed provenance metadata is not a law requiring you to add a footnote. Before assuming a duty is yours, check who it binds. ## The taxonomy of use This is what makes disclosure decidable. Name what the model actually did. | Kind of use | What it means | Where the obligation usually sits | |---|---|---| | **Research assistance** | Finding sources, summarising a field, suggesting search terms. | Usually low. The output is your reading, and everything cited must be verified. | | **Drafting** | The model produced text that survives in the final work. | The main disclosure case in academic and institutional settings. | | **Editing a human draft** | Grammar, tightening, restructuring text you wrote. | Often exempt, and several policies say so explicitly. Check, because the boundary between heavy editing and drafting is where policies differ most. | | **Translation** | Rendering your work into another language. | Increasingly treated as its own category. Some journals require it to be stated, since translation carries meaning decisions. | | **Summarising** | Condensing a source, a transcript, or your own work. | Depends on whether the summary is presented as your reading of the source. If it is, verify it against the source first. | | **Image generation** | Any generated or heavily manipulated visual. | The strictest category almost everywhere. Often prohibited in scholarly figures and usually subject to platform labelling. | | **Data generation** | Producing numbers, tables, synthetic samples, or filling gaps in a dataset. | The most serious category. Generated data presented as measured is fabrication, not a disclosure question. | The reason people cannot answer the disclosure question is that they compress all seven into "I used AI". Separate them and most cases answer themselves: research assistance and light editing rarely require anything, drafting usually requires a statement in a governed setting, generated images require checking before you commit, and generated data requires stopping. ## The responsibility rule **You are accountable for every factual claim in the output regardless of how it was produced.** This single rule resolves most of the cases people find hard, because it separates two questions that get muddled. Disclosure is about the reader's right to know how the work was made. Verification is about whether the work is true. They are independent, and the second is not satisfied by the first. The consequence is blunt: **anything unverified must not ship, disclosed or otherwise.** A footnote saying a model helped write the piece does not license an unchecked citation, an unchecked number, or an unchecked quote. Disclosure is not a warning label that transfers risk to the reader. ## Things that must never be produced Not a disclosure question. These do not become acceptable when labelled. - **Fabricated quotes attributed to a real person.** Including a plausible paraphrase of what they would probably have said, and including public figures. - **Fabricated citations.** A reference that does not exist, or a real reference that does not support the claim attached to it. This is the most common serious failure in generated academic writing and it is checkable in minutes. - **Invented data presented as measured.** Filled-in survey results, plausible benchmark numbers, gap-filled time series. If it was generated, it is not a measurement, and calling it an estimate does not fix it unless the method is stated. - **Synthetic reviews or testimonials.** Covered above under consumer protection rules, and wrong regardless of the rules. - **Impersonation of a real individual's voice or likeness**, in text, audio or image, outside clearly marked satire where the law of your jurisdiction permits it and the marking is impossible to miss. ## The decision rule Work down in order. The first branch that fires is your answer. 1. **A policy applies and names this use.** Disclose exactly as it specifies, in the place it specifies. Do not improve on the wording. 2. **A policy applies but is silent on this use.** Disclose at the quality bar below, and ask the publisher, institution or client in writing. The written answer is the point. 3. **No policy applies, but the work could be mistaken for a first-hand human account, or depicts a real person, or is a review, endorsement or testimonial.** Disclose, or do not publish it. 4. **No policy applies and none of the above is true.** Disclosure is optional. See the good practice section. 5. **You cannot tell whether a policy applies.** Treat it as though it does, and ask before submission. Never resolve this branch by omission, because the cost of an unnecessary disclosure is mild embarrassment and the cost of a missing one, in a governed setting, is a retraction, a misconduct finding, or a regulatory action. ## The disclosure quality bar A real disclosure answers three questions: 1. **Which tool**, named, with the version where the policy asks for it. 2. **For what part of the work**, specifically. Drafting section three. Translating the abstract. Generating the illustration on page two. Not "in the preparation of this work". 3. **Who checked it**, and what they checked. This is the part almost every disclosure omits and it is the part that carries the accountability. A footnote saying that artificial intelligence was used somewhere is not a disclosure. It is a shrug. It gives the reader no way to calibrate anything, and it protects the author rather than informing the audience, which is the opposite of the purpose. A serviceable form: "The first draft of the discussion section was produced with [tool, version] in [month, year]. All sources were retrieved and read by the authors, and all figures are original. The authors reviewed and edited the text and take responsibility for its content." ## When nothing requires disclosure Most writing is in branch four. Good practice there is short. - **Keep a private record** of what was used where. If you are asked in six months, you want an answer rather than a reconstruction. - **Keep the human review step real** rather than nominal. The claim that you checked it should be true. - **Never claim first-hand experience you do not have.** This is the line that matters far more than a footnote. Writing "in the migrations I have run" about migrations you have not run is a lie whether a model wrote the sentence or you did. - **Be more transparent as the reader's decision depends more on a human having done the work.** A product description does not need it. An account of a personal experience, a recommendation, or a professional opinion does. ## The argument against detector-driven writing Detectors have both false positives and false negatives at rates that make them unsafe for consequential decisions about individuals. Three specific things are worth knowing rather than asserting generally. They **misfire on non-native English writers**. A study published in the journal Patterns in 2023, "GPT detectors are biased against non-native English writers", found that detectors misclassified a large share of essays by non-native writers as generated, while classifying native-writer essays correctly. The mechanism is that the detectors keyed on textual simplicity, which is a property of second-language writing as well as of generated text. The vendors themselves have withdrawn products for accuracy reasons. The best-known example is the AI text classifier withdrawn by its own developer in 2023, with low accuracy given as the reason. Several universities disabled detection features in their integrity tooling over the same period after false-positive incidents. And the direction of the error is the problem, not just the rate. A detector that is wrong 5 percent of the time, applied to a thousand students, produces fifty accusations, and the accused has no way to prove a negative. **Optimising a draft against a detector produces worse writing.** The features that lower a score are not the features that make prose good: introducing errors, inflating vocabulary, and breaking rhythm on purpose. You end up with prose that is harder to read and still generated. **The correct response to a detector result is the work itself.** Drafts, notes, version history, the ability to talk about the argument. If you are the one running a detector, treat a score as a prompt to have a conversation, never as evidence. ## Worked example Three decisions, run through the rule. **Case one. A journal submission.** The model was used to find literature and to tighten the discussion section. No generated figures. Taxonomy: research assistance plus editing, with some drafting in the discussion. Branch one, since the journal's author instructions cover generative tools. Action: no authorship, a statement in the acknowledgements naming the tool and version and the sections affected, and every retrieved source independently verified before citation, because the research-assistance category carries the highest fabricated-citation risk. **Verdict: disclose in the acknowledgements, verify all references, no methods entry, because nothing touched the research itself.** **Case two. A product landing page.** The copy was drafted by a model and edited by a marketer. It includes a quote from a named customer. Taxonomy: drafting. Branch four for the copy itself, since no general rule requires disclosing who drafted marketing text. But the quote is a different object. If it came from a real customer with their permission, fine. If it was generated or improved, it becomes a synthetic testimonial and the consumer protection rules apply. **Verdict: no disclosure required for the drafting, and the quote must be verbatim from a real customer with a record of consent, or removed.** **Case three. A blog post with a generated illustration showing a recognisable public figure.** Taxonomy: image generation, depicting a real person. Branch three, and then the never list. The platform's synthetic media labelling policy applies to the image, and the depiction of an identifiable real person is the disqualifying element. **Verdict: do not publish the image. Labelling does not cure it. Replace it with something that does not depict a real individual.** ## Failure modes **The blanket footnote.** "AI was used in the creation of this content." It names no tool, no part of the work, and no reviewer, so it cannot be acted on by anyone. It exists to protect the author. **Disclosure as absolution.** The piece discloses the drafting and ships four unverified citations. The disclosure makes this worse, not better, because it demonstrates the author knew a model was involved and did not check its output. **Disclosing at submission and not at revision.** The tool changed between draft and final, or was used again during revision, and the statement now describes something that is not what happened. **Over-disclosure that hides the material case.** Every trivial use is disclosed at equal weight, so readers stop reading disclosures, and the one that mattered is buried among the spellchecks. **Assuming the provider's duty is yours.** A transparency law binding large model providers gets read as a personal labelling obligation, producing pointless notices, while the actual applicable policy, the publisher's, goes unread. **Confusing permission with disclosure.** The real question was whether the material was allowed into the tool at all: a manuscript under peer review, client data under contract, personal data under a protection regime. Disclosing afterwards does not repair a confidentiality breach. **Detector-driven rewriting.** A draft is degraded on purpose to move a score, and the score is unreliable in both directions anyway, so the writing gets worse and nothing is proven. **Retro-fitting.** A disclosure is quietly added after publication rather than the record being corrected. In a governed setting this is usually treated more seriously than the original omission. ## What this skill does not do - It is not legal advice, and it does not know your jurisdiction, your contract, or your employer's policy. Every branch ends in reading the specific policy that applies to you. - Its policy detail ages. Dates, thresholds and requirements in this area have changed repeatedly and will change again, so treat everything here as a pointer to a source rather than as current fact. - It cannot detect generated text, and it argues that nothing else reliably can either. If you need to establish provenance, the evidence is drafts, history and process, not a score. - It does not cover data protection, confidentiality, or copyright and ownership of output. Those are separate questions and frequently the more serious ones. - It does not verify claims. Checking whether a citation exists and says what you claim is the research category's job, and the claim-and-hedge-audit skill covers the language side of the same problem. - It will not help you evade a policy, a detector, or an obligation. If the honest answer is that the work should not be published as it stands, that is the answer it gives.
These skills all ask your assistant to check things against your actual codebase, your actual schema, your actual design system. Locul keeps that context current on its own, from the files you already have, on your machine. Mac and Windows, free to start.