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Sometimes—but there is no universal switch. An AI-service privacy setting can exclude eligible new conversations or tasks from that provider’s future model improvement. It does not automatically remove a published paper from outside datasets, change a publisher’s contract, or erase training that already occurred.
Your practical options depend on four things: where the material is stored, who controls its rights, whether the use is scientific or commercial text-and-data mining, and which country’s law and publishing terms apply.
What an AI-training opt-out can—and cannot—do
“Opt out” describes several different controls that are often confused. Before taking action, identify the content and the decision-maker for each control point.
| Control point | Content covered | Who controls it | Timing and limits |
|---|---|---|---|
| AI-account data control | Eligible conversations, files, or tasks submitted to one provider | The individual user or workspace administrator | Usually prospective; check product-specific exclusions, feedback rules, and retention terms |
| Institution-managed or business service | Content handled under an organization’s service agreement | Your institution or service administrator | Consumer-account instructions may not apply; verify the exact product tier and sharing settings |
| Publisher or repository reservation | Online versions of a paper or other material within the rightsholder’s control | The publisher, repository operator, or other rightsholder | Requires implementation of notices or technical signals; crawlers are not guaranteed to honor every signal |
| Contract and legal rights | Uses governed by copyright, author agreements, access conditions, and local law | The parties and authorities covered by the relevant jurisdiction | Scope varies by country, purpose of mining, rights ownership, and when a reservation was made |
Changing one control does not change the others. Turning off training for an account does not alter a publisher’s rights in a paper, and adding a website signal does not change what you already uploaded to an unrelated AI service.
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How to stop an AI service from using new account content
ChatGPT individual accounts
OpenAI’s Help Center, accessed October 13, 2026, says an individual user can disable model-improvement use in either of two ways:
- In ChatGPT, open Settings > Data Controls and turn off Improve the model for everyone.
- Alternatively, open the Privacy Portal and select Do not train on my content.
OpenAI states that either route is sufficient for ChatGPT conversations and Codex tasks, and that new conversations are excluded after you opt out. This is a forward-looking control: it is not a promise that material already collected has been removed or that an existing model has been retrained.
Codex environment data
Codex has a separate Include environments setting governing whether full environments can be used for training. Check that setting in addition to the general account control if your work includes repositories, configuration files, or other environment data.
Feedback is a separate path
OpenAI’s guidance says that if you submit thumbs-up or thumbs-down feedback, the conversation associated with that feedback may still be used to improve models even after the general opt-out. Do not attach confidential manuscript text to feedback unless you have confirmed that use is acceptable.
Institutional and business workspaces
Business services have separate default rules and administrative controls. An institutional workspace may be governed by organization-level terms rather than the consumer settings above. Ask your administrator which product tier is active, whether data sharing has been enabled, how long content is retained, and whether administrators can change those settings for the workspace.
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Protect unpublished manuscripts, data and peer-review files
Unpublished material is the point at which a small convenience can create a large confidentiality or intellectual-property problem. Before uploading a manuscript, dataset, grant proposal, or review file, read the provider’s current retention and training terms and check your institution’s policy.
Publisher rules are specific to the publisher
Oxford University Press author guidance tells authors not to upload, in whole or in part, work they intend to publish with OUP into a generative-AI tool that does not permit an opt-out from reuse or training. The guidance also advises opting out of rights that let a provider reuse, distribute, or train on submitted content, or avoiding the tool altogether. Authors with concerns are directed to contact their Acquisitions Editor.
Elsevier’s journal policy, updated June 2026, says submitted manuscripts are confidential and that editors must not upload a manuscript, or any part of it, into AI tools. That is a publisher-specific editorial rule, not a universal policy for every journal or discipline.
A safe pre-upload checklist
- Identify whether the file contains unpublished results, personal data, trade secrets, reviewer identities, or material subject to a funder or collaborator agreement.
- Check the exact service and account tier—not a similarly named consumer product—for retention, training, human-review, and feedback terms.
- Confirm whether your institution requires an approved tool or prohibits external processing of the material.
- Use the provider’s opt-out before uploading when the terms permit it, and document the date and settings.
- Remove unnecessary identifiers and sensitive data; an opt-out is not a substitute for data minimization.
- If a publisher or editor gives a stricter instruction, follow that instruction.
Can a published paper be excluded from outside AI datasets?
Usually, you cannot make a personal account setting reach every copy of a published paper. The publisher, repository operator, or another rightsholder may control the website, copyright, access conditions, and crawler configuration. First determine who controls the version you want to protect and what your author agreement allows.
Ask the rightsholder what has been implemented
Ask the publisher or repository operator whether the site has reserved rights for text and data mining, which versions are covered, and which technical or contractual mechanisms are in use. Your author agreement may assign some rights to the publisher, leave others with you, or impose conditions on sharing a manuscript in a repository. The answer can differ for the version of record, an accepted manuscript, and a preprint.
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Signals publishers can use
The International Press Telecommunications Council (IPTC) best-practice guidance lists several ways a publisher can communicate a reservation or restrict access:
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- Rights and provenance metadata
- Firewalls or authenticated access
- Bot-specific
robots.txtrules - TDMRep declarations
trust.txt- Image metadata
- C2PA assertions
These mechanisms express a rightsholder’s position or limit access; they are not a universal deletion command. IPTC states that there is currently no universal mechanism for adding a “generative AI opt-out” to robots.txt. A site that blocks named crawler user agents must keep those names current, and an unrecognized or non-compliant crawler may not honor the rule.
What EU text-and-data-mining law changes
The European Union’s Digital Single Market rules distinguish scientific research from commercial text and data mining (TDM). The European Commission IP Helpdesk summarizes the distinction as follows:
Article 3: scientific research
Article 3 provides an exception for TDM carried out for scientific-research purposes. In the Helpdesk’s explanation, this exception does not give a rightsholder an opt-out. That does not settle every dispute: eligibility, lawful access, the institution conducting the work, contractual terms, and national implementation still matter.
Article 4: commercial TDM
Article 4 covers broader TDM, including commercial purposes, but allows the rightsholder to limit that use by expressing a reservation in an appropriate manner. The form of an effective reservation depends on the work, access method, and applicable national rules.
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Timing matters
A reservation made after training has taken place does not automatically invalidate or undo that earlier training. Stopping future access and reversing a model’s existing training are separate questions.
There is no single EU registry yet
In 2026, the European Commission announced a feasibility study for an EU-level registry of Article 4 TDM reservations. The announcement means such a registry was being assessed; it does not establish that one active registry already provides a universal opt-out route.
This EU explanation is jurisdiction-specific. It should not be generalized to the United States or other countries, and it is not individualized legal advice. For a dispute, review the governing law, access conditions, author contract, and the exact purpose of the mining with qualified counsel or your institution’s legal office.
How to choose the right action
- Classify the material. Is it a private conversation, a full development environment, an unpublished submission, a peer-review file, a preprint, or a published version of record?
- Locate the decision-maker. For an account, it may be you or an administrator. For a paper, it may be a publisher, repository, funder, or co-rightsholder.
- Check the governing terms. Read the provider’s current data controls, your institutional policy, the publishing agreement, and any repository license.
- Set the control before exposure. Turn off eligible account training before a new upload; ask the publisher or repository to implement a reservation before publication or indexing where possible.
- Record what you did. Save the setting name, date, product tier, notice text, and the URL or policy that supports the action.
- Follow up on exceptions. Confirm whether feedback, administrator sharing, third-party plugins, public mirrors, or separately uploaded files fall outside the control.
Common mistakes and their fixes
Assuming one opt-out covers every copy
Mistake: Turning off an AI account setting and assuming a published article is excluded from web crawls. Fix: Treat account controls and publisher-side reservations as separate tracks, with different owners and legal bases.
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Mistake: Sending an unpublished manuscript to a tool before reading its reuse and training clauses. Fix: Check terms and institutional rules first; if the service cannot provide an acceptable opt-out, do not upload material intended for publication.
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Relying on robots.txt as a guarantee
Mistake: Treating one robots.txt line as a binding, universal AI exclusion. Fix: Combine clear rights language with the technical controls your publisher or site operator supports, and understand that compliant crawling is not guaranteed.
Expecting a later reservation to erase prior training
Mistake: Believing a newly posted reservation automatically changes an already trained model. Fix: Separate prospective prevention from requests concerning previously collected data or model behavior; ask the responsible provider what remedy, if any, is available.
Applying consumer instructions to an institutional account
Mistake: Following individual-account steps in a managed workspace. Fix: Confirm the exact product tier and administrator settings with your institution.
Questions to put to a publisher, repository or service administrator
- Which version of the work do you control, and who owns the relevant rights?
- Has a text-and-data-mining or generative-AI reservation been made, and in what format?
- Which crawlers, APIs, mirrors, and authenticated areas are covered?
- Does the reservation apply only prospectively, or is there a process for previously collected copies?
- For an AI workspace, are conversations, files, feedback, and full environments governed by the same setting?
- Which jurisdiction, contract, or institutional policy governs a disagreement?
Bottom line for researchers
You can often prevent a provider from using new eligible account content for future improvement by changing the provider’s current data-control setting. You cannot assume that this removes a published paper from external datasets, overrides a publisher’s contract, or reverses training that already happened. Protect unpublished work before upload, coordinate published-work reservations with the actual rightsholder, and treat EU scientific and commercial TDM rules as a jurisdiction-specific framework rather than a worldwide opt-out.
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