Check the exact service and model, then read the service’s current data-use controls and the model provider’s training disclosures. These can show what a company says about submitted content and model development, but there is no general public lookup that reliably confirms whether one specific work was in every AI model’s training data.
First, separate submitted content from a model’s past training data
An AI app may use a model built by another company. The app’s terms usually address what happens to material you submit now—such as prompts, uploaded files, or feedback. The model provider’s documentation may describe how that model was developed. These are separate questions, and an answer about one does not establish the answer to the other.
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- Submitted-content use: whether the service retains your inputs, makes them available for human review, or uses them to improve or train models.
- Historical training: what sources were used to train a particular model or version before you used it.
Before checking, write down the app or service name, model name and version if available, account tier, and date. If the app identifies a separate model provider, record that too.
What can each kind of disclosure tell you?
| What you check | What it can tell you | What it cannot establish by itself |
|---|---|---|
| Service terms, privacy policy, and data controls | How the service says it handles content you submit, including retention, training or improvement, review, and any available controls. | Whether your work was used to train a model before you submitted anything. |
| Model documentation and training-content summary | What the model provider discloses about the sources or collections used for training, and which model or version the disclosure covers. | Whether a particular work was included if it is not identified, or whether a disclosed use was lawful. |
How to check a service and model
- Identify the product and model. Note the app, model name and version, subscription or account tier, and date. Look for a model-provider name in the service’s model information or documentation; do not assume the app and model provider are the same company.
- Read the rules for content you submit. In the current terms, privacy policy, and account settings, search for words such as “training,” “improve,” “retain,” “review,” and “data controls.” Check whether the policy distinguishes prompts, uploaded files, feedback, and other data, and whether settings vary by account tier or feature. Record the precise scope of any opt-out or exclusion control.
- Find disclosures for the exact model. Check the model provider’s documentation, model card or equivalent, copyright policy, and any public summary of training content. Look for named datasets, collections, archives, and descriptions of other sources. Note whether the document actually covers the model and version you are investigating.
- Check for a rights-reservation process. If you own or administer the rights, look for whether the provider explains how it recognizes reservations against text and data mining, and what method it accepts. Keep any evidence of a reservation and any provider response.
- Save dated evidence. Keep copies or screenshots of relevant terms, settings, policies, disclosures, model documentation, and correspondence. Record the date you accessed them: policies and models can change.
A setting that limits future use of submitted content is not necessarily a request to remove a work from a model that has already been trained. Ask the provider what the control covers and when it takes effect rather than assuming it changes past training.
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What EU rules require of covered general-purpose AI model providers
The European Commission says providers of general-purpose AI models must have a policy to comply with EU copyright law and related rights, identify and respect rights reservations, and publish a sufficiently detailed summary of training content. These obligations apply from 2 August 2025 to providers placing covered models on the EU market. Models placed on the market before that date must comply by 2 August 2027. The Commission says some documentation obligations may have open-source exemptions, but the copyright-policy and training-summary obligations still apply to open-source providers. See the Commission’s guidelines on obligations for general-purpose AI providers.
The summary is intended to be generally comprehensive in scope, not a technically detailed inventory of every training item. The EU’s Recital 107 points to listing main data collections or datasets and describing other sources in narrative form, while recognizing trade-secret and confidential-business-information concerns. A useful summary may therefore still leave an individual work unconfirmed.
Under the framework described in Recital 105, rightsholders may reserve rights against text and data mining subject to the directive’s conditions. Where rights have been expressly reserved in an appropriate manner, a general-purpose AI model provider needs authorization to carry out text and data mining over those works. This is an EU framework, not a global rule; the relevant facts and applicable law matter.
What the U.S. Copyright Office material does—and does not—settle
The U.S. Copyright Office’s AI study page lists generative AI training as a subject of its report series. The Office labels its Part 3 report, released May 9, 2025, a pre-publication version and says a final version will be published in the future. The report discusses training, licensing, and the EU text-and-data-mining framework, while noting continuing controversy over how exceptions and opt-outs apply to generative AI. It is official analysis, not a final rule resolving every training use or claim. See the U.S. Copyright Office AI study and its Part 3 pre-publication report.
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The Office reported receiving over 10,000 comments by the December 2023 deadline for its AI study. That figure measures public submissions to the study, not the number of works used in training or a count of legal positions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret an inconclusive answer
A public summary may identify broad sources without naming every work. A provider may also publish a policy that does not explain how it handled one specific item. In either case, an omission from the disclosure is not proof that the work was excluded, just as a general disclosure is not proof that the work was included. A provider statement, opt-out control, or disclosure alone also does not decide whether a particular use was lawful.
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If the answer affects a licensing or enforcement decision, preserve the relevant documents and correspondence, ask the provider a work-specific question, and get advice for the jurisdiction and contract involved. The legal outcome can depend on the work, the use, the reservation made, and the applicable law.
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