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If an AI company may have copied, trained on, fine-tuned with, or reproduced your work without permission, first preserve evidence and identify exactly what happened. Then check who owns the relevant rights, ask the company for its legal or licensing basis and available removal process, and obtain advice from an intellectual-property lawyer or creators’ rights organization before making a legal demand. Whether the conduct is lawful depends on the country, the work, the way it was obtained and used, and whether a generated output reproduces protected expression.
Start by identifying the precise use
“An AI company used my work” can describe several different events. They are not interchangeable in a legal analysis, and the evidence needed for each one differs:
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- Dataset collection: a copy may have been downloaded, scraped, stored, or indexed before any model was trained.
- Model training: the work may have been processed as an example used to adjust model parameters.
- Fine-tuning or evaluation: the work may have been used to adapt or test a model or a specialized service.
- Generated output: a user may have obtained an image, text, recording, code sample, or other result that reproduces all or a substantial part of your protected work.
The UK government’s March 2026 report on copyright and artificial intelligence treats training-stage copying and output-stage reproduction as separate questions. Evidence that a work appeared in a dataset does not, by itself, prove that a particular output infringes; an apparently infringing output does not, by itself, establish how the training data was obtained.
Write down the facts before contacting anyone
Make a short chronology while information is fresh. Record the exact title or file name, creator, creation and publication dates, public URLs, the company and product involved, the country or countries concerned, and how you learned of the alleged use. If an output is involved, save the complete output, prompt or input, account or service context, date and time, and any settings that affect the result. Note whether the result is an exact copy, a substantial portion, or only a general similarity; those descriptions lead to different legal questions.
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Do not describe an inference as a fact. For example, a search result showing that a page was accessible to a crawler may establish availability, not necessarily that the page entered a particular training run. Keep separate notes for what you observed, what the company stated, and what remains unknown.
Preserve original and rights evidence
Keep the original, uncompressed files and dated backups. Save screenshots and, where possible, an archived copy of the page showing the work, its copyright notice, terms, and access date. Preserve notices, invoices, registrations, contributor agreements, publisher or agency contracts, employment records, and every message exchanged with the company. Retain the full terms of service and privacy or dataset notices that applied when the work was uploaded or made public; terms can change.
Build a rights chain: list coauthors, employers, commissioning clients, publishers, agencies, assignees, and exclusive licensees. A person who created a work may not be the current owner of every relevant right. This checklist is practical documentation, not a government-mandated universal proof package. The U.S. Copyright Office advises people who believe their copyright was infringed to consult an attorney; an attorney can tell you which records matter in your jurisdiction.
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Check ownership, contracts, and permissions
Read the agreement that governed creation and publication. Look for assignments, exclusive licenses, work-made-for-hire or employment clauses, sublicensing rights, warranties, and permissions granted to platforms that hosted the work. Also check whether a client, publisher, or collective management organization controls enforcement. If another party owns the copyright, coordinate with it before sending a demand or accepting a settlement.
Then identify the permission the company claims, if any. It might rely on a direct license, a platform’s terms, a public-domain status, an open license, a statutory exception, or a fair-use defense. A work being publicly viewable is not the same as a blanket license for every downstream use. Conversely, the absence of an individual permission request does not automatically establish infringement: exceptions and defenses vary by country and facts.
How the legal analysis differs by jurisdiction
The applicable law normally follows the relevant acts, parties, and territories, not simply where you live. A service may collect a work in one country, train infrastructure in another, and offer an output globally. Get local advice before relying on a conclusion drawn from another country.
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| Jurisdiction | Relevant framework described by official sources | Practical significance |
|---|---|---|
| United States | Fair use is a case-by-case defense. The factors include purpose and character (including commercial or nonprofit purpose and transformation), nature of the work, amount and substantiality used, and effect on the market. The U.S. Copyright Office says AI-training fair-use questions remain unsettled. | A court would weigh the particular work, copying, purpose, technology, and market evidence. There is no categorical rule that all training is fair use or that all unlicensed training is infringement. |
| European Union | The UK government report describes two text-and-data-mining (TDM) exceptions that require lawful access. The general-purpose exception includes a rightsholder opt-out; for online works, that reservation must be machine-readable. | Whether access was lawful and whether a valid, technically expressed reservation existed can be important. The exception’s scope and national implementation should be checked for the relevant member state. |
| United Kingdom | The report describes no broad TDM exception and no general U.S.-style fair-use principle. | Permission, another specific exception, or a license may be central. The report also describes unresolved questions and developing litigation, so outcomes remain fact-specific. |
The comparison above comes from the UK government’s March 2026 report. It is a map of issues, not a prediction of a particular case. Rules and court decisions can change.
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- Identify the work and rightsholder. Gather the exact files, versions, authorship information, ownership documents, and any exclusive rights. Name the AI company, product, account, and suspected use. Mark the relevant dates and territories.
- Separate training evidence from output evidence. For a dataset allegation, document the location, access conditions, file identity, and statements linking the work to collection or training. For an output, preserve the output and the complete prompt and explain precisely which protected expression appears.
- Preserve an immutable record. Keep dated originals, screenshots, URLs, terms, notices, and correspondence in more than one secure location. Do not alter the only copy of a file or rely on a cropped screenshot that removes context.
- Review the applicable permission or exception. Check licenses, platform terms, public-domain status, open-license conditions, lawful-access requirements, and any machine-readable rights reservation. Record the version and date of each term.
- Seek tailored advice. An intellectual-property lawyer, creators’ rights organization, union, or collective management organization can assess jurisdiction, ownership, evidence, limitation periods, likely defendants, and cost. Advice is especially important before alleging infringement publicly or signing a release.
What to ask the AI company
Use a factual, narrow request and attach only material needed to identify the work. Ask:
- Whether the company collected, stored, trained, fine-tuned, evaluated, or otherwise processed the identified work.
- Which product, dataset, model version, date range, and legal or licensing basis are involved.
- Whether the company can identify the source and access conditions under which it obtained the work.
- Whether it offers a process to remove the work from a dataset, service, or future training run, and what that process actually covers.
- Whether it will discuss a license, attribution, or compensation arrangement, and whether any proposal covers past use, future use, or both.
- How to preserve a complaint, appeal a decision, or obtain a written response.
Removal, licensing, and payment are not guaranteed rights in every country. The reviewed official materials do not establish one universal AI-training takedown procedure or a guaranteed compensation process. Treat a company’s response as evidence to preserve, not as a substitute for independent legal advice. Do not upload additional high-resolution material merely to “test” a system unless your adviser considers that safe.
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Training use and an infringing output are different claims
A model may have been trained on a work without producing a recognizable copy. Conversely, an output can reproduce protected expression even when you cannot prove that your file was in the training set. The UK report states that an output reproducing all or a substantial part of a protected work may infringe when no applicable exception or license applies. Responsibility can depend on who generated, supplied, distributed, or otherwise dealt with the output.
Describe the similarity precisely. Preserve side-by-side copies, metadata, the generation prompt, account identity where lawfully available, and distribution records. A general resemblance, an idea, or a style reference is not the same allegation as reproduction of protected expression. The legal test is fact-specific, and a lawyer can help distinguish copyright from trademark, contract, privacy, or publicity issues that may also arise.
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If you are considering formal enforcement
Choose the forum and parties carefully
Potential defendants may include a dataset operator, model developer, service provider, distributor, or a person who supplied or commercialized the output. The correct defendant and court depend on contracts, locations, jurisdictional contacts, ownership, and the conduct you can prove. Do not assume that the company whose interface generated an output is the only responsible party.
In the United States, the U.S. Copyright Office’s Fair Use FAQ states: “A party may seek to protect his or her copyrights against unauthorized use by filing a civil lawsuit in federal district court.” The same FAQ says: “If you believe that your copyright has been infringed, consult an attorney.” A civil filing is one possible route, not a promise of success, damages, an injunction, or recovery of legal fees.
The UK report describes civil courts as an enforcement route for relevant copyright infringement. Other countries have their own courts, procedures, registration rules, evidence standards, and deadlines. Ask counsel about limitation periods, registration or recordation requirements, preservation letters, jurisdiction, discovery, interim relief, and the economic practicality of a claim before escalating.
Consider non-court options
A negotiated license, correction, removal request, or settlement may resolve a dispute more quickly than litigation, but read any release carefully. Check whether it waives claims for earlier copies, future model versions, outputs, sublicenses, or unknown uses. A collective or union may offer a coordinated approach where many rightsholders report similar conduct. Keep your own records even if the company agrees to a voluntary change.
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- Do not assume that public availability equals permission for unrestricted AI training.
- Do not assume that every training use without an individual permission request is automatically infringement.
- Do not assume that proving dataset inclusion proves an infringing output, or that an output proves dataset inclusion.
- Do not assume a machine-readable opt-out works everywhere; its relevance depends on the jurisdiction, exception, work, and technical implementation.
- Do not assume a demand for payment creates an entitlement to compensation. Licensing is part of policy debate, but a proposal to license is not itself a legal finding.
- Do not publish accusations before checking ownership, evidence, and defamation or contract risks with counsel.
Why the answer may change
Official policy and case law are still developing. The U.S. Copyright Office’s Copyright and Artificial Intelligence index lists reports in parts: Part 1 on digital replicas, Part 2 on copyrightability of AI outputs, and Part 3 on generative-AI training. The page identifies Part 3 as a pre-publication report released May 9, 2025 and says a final version is forthcoming. Check the index and current court decisions before relying on an older summary.
In the United Kingdom, a 2025 government progress statement reported more than 11,500 consultation responses: just over 10,100 online and more than 1,400 by email. It said 88% of online survey respondents supported requiring licenses in all cases. That figure describes respondents to the government’s online survey service; it is not a representative poll of all UK creators or the public, and it does not itself create a licensing requirement.
A compact decision checklist
- Can you identify the exact work, version, creator, and current rightsholder?
- Do you have dated originals, publication records, URLs, screenshots, terms, and correspondence?
- Are you alleging collection, training, fine-tuning, output reproduction, or more than one stage?
- Which country’s law, contracts, and courts are plausibly involved?
- Is there a license, lawful-access basis, rights reservation, public-domain status, or other exception to examine?
- Have you preserved the complete output and prompt context rather than only a screenshot?
- Have you obtained advice before making a public accusation, signing a release, or filing a claim?
Bottom line
Creators have practical steps even though the law is unsettled: identify the exact act, preserve a credible record, verify the rights chain, ask the company for its basis and available remedies, and obtain jurisdiction-specific advice. Training-stage copying and output-stage reproduction must be analyzed separately. A documented, carefully scoped complaint gives you a stronger foundation for a voluntary resolution or, where justified, formal enforcement.
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