The Tool Desk
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The most reliable way to reduce the chance that an unpublished manuscript is used to train an AI model is to avoid uploading it to any service whose current terms, retention rules, human-access practices, or opt-out controls you cannot verify. Keep a controlled master copy, submit only the minimum text needed, obtain written assurances from editors and vendors, and negotiate explicit contract language covering AI training and related uses. U.S. copyright protects your original writing once it is fixed, but it does not create a technical barrier that stops copying, scraping, or uploading.
Before sharing a manuscript, treat the decision as both a data-governance question and a rights question. Check the AI service’s current terms, ask who may access or reuse the text, review your publishing agreement, and decide whether copyright registration fits your timing and risk. The safeguards below reduce exposure; none guarantees that a determined person or service cannot copy a file.
Start with a no-upload rule for unclear services
If a tool’s terms do not clearly explain whether submitted text is retained, reviewed by people, reused to improve models, or excluded from training, do not submit an unpublished manuscript. Oxford University Press advises authors not to upload intended-for-publication work to tools that do not permit opting out of reuse or training. Elsevier likewise tells authors to confirm that a tool does not train on submitted material and to avoid restricted or sensitive content unless confidentiality and the terms are satisfactory. These are publisher-specific policies, but the underlying precaution applies to any consumer or workplace service.
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A service can change its defaults without changing its name. Recheck the terms and settings immediately before a significant upload, even if you used the same tool previously. Save a dated copy or screenshot of the relevant settings for your records; that documents what you relied on, although it does not alter the provider’s contract.
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Use this pre-upload checklist
- Identify the exact data. Decide whether the task requires the full manuscript, a chapter, a paragraph, a synopsis, or only your own question. Remove material that is irrelevant to the task.
- Read the current terms and privacy controls. Look specifically for model training, service improvement, data reuse, retention periods, deletion, human review, subcontractors, and disclosures to affiliates or other users.
- Check the account type. Consumer, team, enterprise, education, and API accounts can have different defaults. Do not assume that a paid account automatically provides confidentiality or a training opt-out.
- Find the opt-out scope. Determine whether an opt-out covers future submissions only, data already collected, manual review, model training, or all secondary uses. Record the date on which you enabled it.
- Assess deletion. Confirm how to delete the conversation or file, whether backups persist, and whether deletion removes data from logs or improvement datasets.
- Check human access. A provider may permit limited human review for safety, debugging, or quality. Decide whether that is acceptable for unpublished work.
- Use the smallest useful excerpt. A style question may need a few paragraphs, not a whole novel. Replace names, addresses, plot secrets, and third-party confidential material with neutral placeholders when the task allows.
- Keep a record. Note the service, account, setting, date, purpose, and exact portion submitted. Keep the clean master copy outside the service.
If any material answer remains unclear, use a non-upload workflow: work from your own notes, use locally controlled software, or ask a qualified editor to perform the task under a written confidentiality obligation.
Understand what an AI service can do with submitted text
“Not used to train the model” is narrower than “confidential.” A provider might exclude prompts from training while retaining them for a stated period, allowing safety review, using them for analytics, or sharing them with processors. Conversely, a training opt-out may not cover a file that a contractor downloads manually. Evaluate the whole data path rather than relying on a single toggle.
| Question to answer | Why it matters | Evidence to look for |
|---|---|---|
| Are submissions used for model training? | Training can expose text to model-development processes beyond the immediate task. | Terms that expressly exclude your content, or a documented opt-out that applies to your account and submission type. |
| Is data reused for service improvement? | “Improvement” may include review, analytics, evaluation sets, or future products even when “training” is not mentioned. | A definition of improvement and a clear exclusion or permission requirement. |
| How long is content retained? | Longer retention increases the period in which an account breach, employee access, or legal demand could expose the file. | Retention and deletion periods, including backups and abuse-monitoring logs. |
| Can people read it? | Human review creates a different confidentiality risk from automated processing. | Limits on reviewer access, purposes, geography, and whether you can disable review. |
| Who else receives it? | Processors, contractors, and subprocessors may be bound by different operational controls. | Subprocessor disclosures and contractual confidentiality commitments. |
| What account or region applies? | Consumer and organizational plans, and different jurisdictions, may have different defaults. | The terms linked from the actual sign-in or admin console you will use. |
Wiley, Oxford University Press, and Elsevier each direct authors to inspect safeguards or terms in their respective publishing contexts. Their guidance should not be read as a universal promise by every AI provider. The practical test is whether the service’s own current terms give you a sufficiently clear and enforceable basis for the particular manuscript.
Ask everyone in the publishing chain before they upload anything
Your manuscript may reach an AI system even if you never open one yourself. Editors, agents, copy editors, translators, proofreaders, publicists, designers, and other contractors may use automated tools for summaries, evaluations, grammar work, metadata, marketing copy, translation, narration, or workflow management. Ask each participant what systems they use and whether your text or personal information is sent to them.
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Questions for an editor, agent, or vendor
- Will any part of the manuscript or author information be entered into an AI or machine-learning service?
- What exact purpose does the use serve, and is a human expected to review the output?
- Which provider, account type, and geographic region will process the material?
- Is training, model improvement, data mining, or secondary reuse disabled in writing?
- How long are prompts, files, outputs, and backups retained, and how are they deleted?
- Can subcontractors access the material, and do they have equivalent confidentiality duties?
- Will the resulting summary, translation, assessment, or derivative output be stored or reused?
Request answers before delivery, not after a manuscript has circulated. The Authors Guild’s April 2026 model language proposes written permission before consumer AI systems are used on a manuscript or an author’s personal information, together with training opt-outs when such use is permitted. It is a negotiation resource, not a mandatory rule or a substitute for reviewing your actual agreement.
Negotiate an AI clause instead of relying on a general confidentiality paragraph
Older publishing agreements may grant broad rights to reproduce, distribute, adapt, translate, create summaries, or sublicense a work without mentioning AI. Whether a particular clause covers model training or data mining can be disputed. Ask for explicit boundaries so that the contract records what is allowed, what is prohibited, and when your consent is required.
| Contract subject | Points to clarify |
|---|---|
| AI training and data mining | State whether the manuscript, metadata, or author information may be used to train, fine-tune, evaluate, or develop a model. If permitted, require a defined purpose, provider, account, retention period, and no-training setting. |
| Internal tools | Define whether the publisher may use a private system for proofreading, search, indexing, or workflow automation, and whether the system’s operator may retain the inputs. |
| Summaries and assessments | Specify who may generate reader reports, sales summaries, editorial evaluations, or synopses, and whether those outputs can be shared outside the business. |
| Adaptations and derivatives | Address translation, narration, audio generation, illustrations, interactive versions, and other outputs that may go beyond clerical assistance. |
| Marketing and metadata | Clarify whether AI may draft advertising, retailer descriptions, author biographies, keywords, or social posts from the manuscript. |
| Sublicensing | Require disclosure of any third party receiving manuscript data and prevent a sublicense from granting broader AI rights than the publisher has. |
| Consent and notice | Set a written-consent process, the information you receive before approval, and a right to withdraw permission for future uses where feasible. |
| Security and deletion | Require reasonable security, access controls, incident notice, return or deletion of files, and confirmation when the editorial project ends. |
Have a qualified lawyer or literary-rights professional review consequential language. A model clause can identify issues to negotiate, but it cannot determine what your publisher’s existing grant means under the governing law.
Know what U.S. copyright protects—and what it cannot stop
Under U.S. law, copyright generally exists automatically in an original work of authorship once it is fixed in a tangible medium. The U.S. Copyright Office describes fixation as the point at which the work is recorded in a sufficiently stable form. You do not have to publish the manuscript to obtain that protection.
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Copyright gives the owner exclusive rights such as reproduction, distribution, public display, and preparation of derivative works, subject to statutory limitations. It does not function like a password or a digital rights-management system. Someone can still copy a file, paste it into an AI service, or scrape a server; the practical question then becomes whether the conduct violates a copyright, contract, confidentiality duty, privacy law, or another rule.
Registration is separate from automatic protection
Registration creates a public record and can provide important enforcement benefits. For a U.S. work, registration or refusal is generally required before filing an infringement action in federal court. Timely registration can also affect eligibility for certain statutory damages and attorney-fee remedies. The Copyright Office offers electronic registration through its eCO system.
| Step or status | What it means | Important limitation |
|---|---|---|
| Fix the manuscript | Original expression is generally protected automatically when recorded. | Protection does not technically prevent copying or uploading. |
| Register with the Copyright Office | Creates a public record and may preserve litigation and remedy options. | Registration information and submitted material become part of a public record; publication is not required. |
| Suspect misuse | Preserve evidence and assess contract, copyright, confidentiality, and jurisdictional issues. | Whether a specific training use infringes is fact-specific and cannot be resolved by a publisher policy alone. |
The Office warns that registration records are publicly available online. Weigh that disclosure against the enforcement advantages, especially if the manuscript contains personal information or material you do not want placed in a public record. Registration timing can matter, so obtain advice for your circumstances rather than treating it as an emergency substitute for access controls.
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Good file hygiene cannot make a manuscript immune to copying, but it limits unnecessary exposure and helps establish what was shared.
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- Keep one clean master in storage you control, with strong authentication and backups.
- Create task-specific copies that contain only the pages or passages required.
- Remove tracked changes, comments, embedded metadata, contact details, and unrelated chapters before sending a file.
- Use access-controlled links with expiration dates instead of unrestricted public links where possible.
- Record recipients, dates, versions, and the purpose of every transfer.
- Use confidentiality agreements for freelancers and consultants whose work is not already covered by your publishing contract.
- Revoke access and request deletion when an assignment ends; retain your own transfer record.
- Do not place a full manuscript in a public demonstration, forum, shared prompt library, or repository merely to obtain feedback.
Encryption, passwords, and access controls protect a file in transit or storage; they do not override a recipient’s contractual right to reuse content after the recipient decrypts it. Technical measures and contract terms therefore need to work together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a “no AI training” notice can and cannot do
A notice in a manuscript, email, or submission portal can communicate your position and support a later argument about permission. It can say that the text may not be used to train, fine-tune, evaluate, or improve an AI system without written consent. However, no official source reviewed for this guidance establishes that a notice alone prevents copying, scraping, or training, and a notice cannot change terms you already accepted. Treat it as a signal and negotiation point, not a technical block or guaranteed legal remedy.
Put the operative restriction in the agreement or service setting that governs the transfer. If a platform offers a contractual opt-out, enable it before uploading and keep evidence of the setting. If a publisher refuses to define its AI practices, decide whether the value of the arrangement justifies the unresolved exposure and obtain professional advice.
If you suspect your manuscript was used improperly
- Preserve evidence. Save the original file, hashes or version history, submission receipts, screenshots of terms and settings, correspondence, and any output that appears to reproduce distinctive text.
- Map the disclosure. List who received which version, under what agreement, through which service, and on what date.
- Do not make public accusations first. Public posts can complicate negotiations, reveal additional text, or create defamation risk.
- Notify the relevant counterparty. Ask the publisher, vendor, or service for preservation of logs, the identity of processors, the applicable terms, and the steps taken to stop further use.
- Get jurisdiction-specific advice. A lawyer can evaluate copyright registration, contract remedies, confidentiality, privacy, and cross-border issues. The legality of a particular training use depends on the facts and governing law.
- Consider registration and enforcement timing. If you have not registered, discuss whether filing now improves your position; registration does not erase a prior disclosure.
A decision framework for common situations
You want feedback on a few paragraphs
Use the smallest excerpt, remove identifying details, verify that the selected account excludes training and unnecessary retention, and save the terms and settings you relied on. If those controls are unclear, ask a human reviewer under confidentiality terms instead.
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You want a full-manuscript developmental analysis
Assume the exposure is high. Prefer a service with clear contractual restrictions, suitable account controls, defined deletion, and no human review unless you expressly approve it. A publisher or editor should obtain your written permission before submitting the manuscript.
Your publisher says it uses an internal AI system
Ask whether the system is operated by the publisher or a third party, whether inputs leave the publisher’s controlled environment, whether training and improvement are disabled, who can review prompts, and when files are deleted. Put the answers and your consent limits in writing.
You already uploaded the manuscript to an unclear service
Stop further submissions, export the relevant records, disable any available reuse or training setting, request deletion under the current terms, and document the request. Then obtain advice about contractual and legal options; do not assume that deletion guarantees removal from backups or previously created datasets.
What protection looks like in practice
No single measure solves this problem. The strongest practical posture combines four layers: minimize what you disclose, verify the recipient’s controls, reserve AI rights expressly in contracts, and maintain evidence that supports enforcement if misuse occurs. Copyright registration can strengthen a U.S. enforcement position, but it is not a substitute for confidentiality, careful sharing, or clear permission.
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