Sometimes—but there is no universal switch. A researcher can usually stop a particular AI service from using eligible new conversations or tasks for future model improvement. That setting does not remove a published paper from external datasets, change a publisher’s contract, or undo training that already happened. For published research, control depends on copyright and contract terms, who operates the website or repository, technical rights reservations, the purpose of text-and-data mining (TDM), and the jurisdiction involved.
What “opting out” can—and cannot—do
“AI training data” covers several different pathways. Keeping these pathways separate prevents a setting that solves one problem from being mistaken for a complete withdrawal.
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Content submitted to an AI service
An account or workspace data control can govern prompts, uploaded files, conversations, or coding tasks sent to that provider. The control is normally prospective: it excludes new eligible content after you turn it on. It does not necessarily erase material the provider already collected, and it cannot control copies held by another service.
Published papers and research data
A paper on a journal site, repository, laboratory server, or personal website may be copied into datasets by organizations that the author does not control. Whether you can reserve rights depends on the author agreement, copyright ownership, access conditions, technical signals, and applicable law. An author who transferred copyright may not be able to make the reservation alone.
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Training that already occurred
Stopping future collection is different from reversing an existing model’s training. A later notice or rights reservation does not automatically invalidate or undo training that took place earlier. Ask the provider or rightsholder what deletion, exclusion, or retraining process—if any—actually exists; do not assume that an opt-out rewinds a model.
How to limit use of content in an AI-service account
Use the controls for the exact product, account type, and workspace you use. Labels and scope can change, so verify the current terms before uploading sensitive work.
ChatGPT individual accounts
OpenAI’s Help Center says an individual user can turn off Improve the model for everyone in Settings > Data Controls, or choose Do not train on my content in the Privacy Portal. OpenAI states that either route is sufficient for ChatGPT conversations and Codex tasks, and that new conversations are excluded after you opt out. The current instructions are documented at OpenAI’s data-use help article (accessed October 13, 2026).
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- Open Settings, then Data Controls.
- Turn off Improve the model for everyone, or use the Privacy Portal option labeled Do not train on my content.
- Confirm the setting before starting a new conversation or upload. Keep a record of the date and account if your institution requires an audit trail.
The setting applies to eligible new content; it is not a promise that previously submitted conversations disappear. OpenAI also says that if you submit thumbs-up or thumbs-down feedback, the conversation associated with that feedback may still be used to improve models. Avoid rating a conversation containing confidential material unless you have checked that exception and accepted it.
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Codex environments
Codex has a separate control for whether full environments are included in training. Check the Include environments setting in addition to the general account control. A researcher who disables conversation improvement but leaves environment inclusion enabled could still share a broader project context than intended.
Institutional and business workspaces
Do not apply consumer-account instructions automatically to an institutional or business service. Organization-level terms, administrator settings, retention policies, and product tiers can govern whether content is used for service improvement. Ask your administrator which agreement applies, whether data sharing is enabled, how long uploads are retained, and whether individual users can change the setting. Record the answer for projects involving unpublished results or controlled data.
Before uploading an unpublished manuscript or peer-review file
Unpublished work is usually the highest-risk category because disclosure can affect novelty, confidentiality, intellectual-property rights, or a journal’s submission rules. A service that offers convenient drafting is not automatically acceptable for a manuscript, dataset, grant application, or review report.
Check four things first
- Training and reuse: Does the provider permit you to opt out of reuse or training, and does the control cover the exact product and account?
- Retention: Can the provider retain prompts, files, logs, or feedback after you delete a conversation?
- Confidentiality: Does your institution, funder, data-use agreement, or peer-review policy prohibit uploading the material?
- Publication contract: Does the target publisher restrict generative-AI tools or require disclosure?
Oxford University Press guidance
Oxford University Press (OUP) 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 opting out of reuse or training. Its guidance says: “To protect the value of your work and avoid compromising your intellectual property rights, you must not upload any work you intend to publish with OUP, in full or in part, into any Gen AI tool that does not permit you to opt out of re-use or training in this way.” OUP also advises authors to opt out of rights grants where the terms allow a provider to reuse, distribute, or train on submitted content. Read the full policy at OUP’s author AI guidance, and contact your Acquisitions Editor when the policy or your contract is unclear.
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Elsevier journal submissions
Elsevier’s journal policy says submitted manuscripts are confidential and that editors must not upload manuscripts, or any part of them, into AI tools. The policy is publisher-specific, not a universal rule for every journal, but it illustrates why a researcher should check the destination journal’s current requirements before using an AI service. See Elsevier’s generative-AI policy for journals (updated June 2026).
What authors can do about papers already online
Start by identifying who controls the relevant copy and rights. A researcher may own a preprint but not the version of record, or may have transferred rights to a publisher. A repository administrator may control crawler access even when the author owns the text.
Ask the right questions
- Which version is being exposed: publisher version, accepted manuscript, preprint, supplementary data, or an institutional copy?
- Who owns or administers that copy, and what does the publishing agreement say about text and data mining?
- Has the site already published a rights reservation, metadata instruction, or crawler policy?
- Which uses are being restricted—commercial model training, all TDM, image reuse, or only automated access?
When the answer is uncertain, contact the publisher or repository operator rather than placing an author-only notice on a page you do not control.
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IPTC’s Generative AI Opt-Out Best Practices (May 2025) lists several mechanisms that publishers can combine:
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| Mechanism | What it communicates | Important limitation |
|---|---|---|
| Visible rights notice | A human-readable reservation or permission statement on the page | Relies on users and systems interpreting the wording correctly |
| Metadata | Machine-readable rights information attached to content | Can be stripped when files are copied or transformed |
| Firewalls and access controls | Restricts automated or unauthenticated retrieval | May block legitimate readers and does not govern authorized copies |
| Bot-specific robots.txt rules | Asks named crawlers not to fetch specified paths | Bot names must be maintained; compliant systems may still be only a subset of crawlers |
| TDMRep | A structured way to express TDM permissions or reservations | Adoption and interpretation vary between services |
| trust.txt | Publishes information about trusted relationships and site policy | It is not a universal training prohibition |
| Image metadata | Carries rights information with image files | Metadata can be removed during editing or reposting |
| C2PA assertions | Cryptographically associated provenance and rights claims | Coverage depends on compatible tools and preserved assertions |
IPTC states that there is currently no universal way to add a “generative AI opt-out” to robots.txt. A site can block individual crawler user agents, but it must keep the list current, and a technical signal is a reservation or access instruction—not a guarantee that every crawler will detect or obey it.
How EU text-and-data-mining rules affect an opt-out
The European Commission IP Helpdesk describes two provisions of the EU Digital Single Market Directive that treat scientific and commercial TDM differently. This is an EU-specific explanation, not a worldwide rule or individualized legal advice.
| EU provision | Use covered | Opt-out position described by the Helpdesk |
|---|---|---|
| Article 3 | Text and data mining for scientific research by eligible research organizations and cultural-heritage institutions | The exception does not provide a rightsholder opt-out, subject to the provision’s conditions and national implementation |
| Article 4 | Broader TDM, including commercial purposes | Rightsholders can limit the exception by reserving rights in an appropriate, machine-readable manner |
Read the Commission’s explanation at Europe—Frequently Asked Questions. The practical result is that “I reserved rights” may have a different effect depending on whether the mining falls under the scientific-research or commercial provision, whether access conditions were met, and which national law implements the directive.
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The Commission announced a feasibility study for an EU-level registry of Article 4 TDM opt-outs in 2026. The announcement at New feasibility study for introducing an EU-level registry of Text and Data Mining opt-out means a registry was being assessed; it does not establish a single active registry that currently handles every reservation.
A practical decision path for researchers
Use this sequence when deciding whether to upload, publish, or reserve rights.
- Classify the material. Mark it as confidential and unpublished, under review, published by someone else, or published on a site you control.
- Identify the decision-maker. For an AI account, it may be you or a workspace administrator. For a paper, it may be the publisher, repository, or copyright owner.
- Read the controlling terms. Check the AI service’s training, reuse, retention, feedback, and deletion terms; then check institutional rules, funder conditions, and the publishing agreement.
- Apply the narrowest effective control. Turn off the relevant account setting, disable Codex environment inclusion, restrict workspace sharing, or ask the site operator to publish an appropriate reservation.
- Document scope and date. Save the setting name, account or site covered, date enabled, and any exception. This helps demonstrate what you requested without claiming more than the control provides.
- Escalate rights questions. Ask a research-office, librarian, publisher, or qualified lawyer when ownership, jurisdiction, or the scientific-versus-commercial TDM classification is disputed.
Common failure modes
“I opted out, so my published paper is removed everywhere”
An account setting affects content sent to that account. It does not control a publisher’s website, a repository mirror, a search index, or a dataset assembled before the setting changed.
“robots.txt is a universal legal block”
It is not. Robots rules are technical instructions whose coverage depends on crawler behavior and maintained user-agent names. Pair them with clear rights language and other controls where appropriate.
“The author can reserve rights even after transferring copyright”
Not necessarily. Review the publishing agreement and ask the rightsholder or site operator who can make the reservation for each version.
“A publisher policy applies to every journal”
Policies differ by publisher, journal, role, and date. Read the policy for the specific venue. The American Association of University Professors also maintains guidance on AI in academic research and publishing at its academic AI resource, but it does not replace a journal’s contract or institutional rule.
The bottom line
Researchers can reduce future use of their material, but the control is always bounded. For new uploads, use the exact AI service’s data setting and verify exceptions such as feedback and Codex environment inclusion. For unpublished work, do not upload until confidentiality, retention, training, and publisher rules are acceptable. For published work, determine who controls the rights and combine contractual, plain-language, metadata, and technical measures where they fit. Finally, treat EU Article 3 and Article 4 rules as jurisdiction-specific and remember that preventing future access is not the same as undoing earlier training.
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