Skip to content
AI Act Recital 107 EN
LLM context A cited markdown file you can paste into your AI assistant (ChatGPT, Claude, a RAG or project knowledge base) to ground it in this law. Contains: the full text of every article, recital and provision of this law. Everything links back to its source on overview.legal — legal information, not advice.

Recital 107 — transparency training data summary

In force — consolidated2026-07-27 · CELEX 02024R1689-20260727 · ELI ↗
Version history 2
  • 2026-07-27in force CELEX 02024R1689-20260727
  • 2024-07-12 CELEX 02024R1689-20240712

In order to increase transparency on the data that is used in the pre-training and training of general-purpose AI models, including text and data protected by copyright law, it is adequate that providers of such models draw up and make publicly available a sufficiently detailed summary of the content used for training the general-purpose AI model. While taking into due account the need to protect trade secrets and confidential business information, this summary should be generally comprehensive in its scope instead of technically detailed to facilitate parties with legitimate interests, including copyright holders, to exercise and enforce their rights under Union law, for example by listing the main data collections or sets that went into training the model, such as large private or public databases or data archives, and by providing a narrative explanation about other data sources used. It is appropriate for the AI Office to provide a template for the summary, which should be simple, effective, and allow the provider to provide the required summary in narrative form.

Related across sources

Victory! Appeals Court Rejects Expansive New Copyright Claim The U.S. Court of Appeals for the Ninth Circuit handed internet users and programmers a big win today, by rejecting an attempt to stretch a narrow provision of the Digital… Sep 16, 2026 Data Governance for AI Training Data Requirements
2025 AI: complex algorithms and effective data protection supervision EDPB/SPE 23 jan 2025 AI: complex algorithms and effective data protection supervision. Een rapport opgesteld door Kris Shrishak (ICCL/Enforce) waarin met name bias assessment en… EDPB Jan 23, 2025 Training Data Requirements Data Governance for AI AI Governance Framework
2025 Generative AI and data protection Hannah Ruschemeier — Cambridge Forum on AI Law and Governance Cambridge Forum on AI Law and Governance ·full text Jan 1, 2025 Training Data Requirements Data Governance for AI Artificial Intelligence
2026 General-Purpose AI under the EU AI Act: A Conceptual Allocation of Duties across the Value Chain Fabian Teichmann — SCRIPTed A Journal of Law Technology & Society SCRIPTed A Journal of Law Technology & Society ·full text Jun 30, 2026 AI Governance Framework AI Value Chain Actors and Roles AI Impact Assessment