In the European Union, machine-learning training informs several different decisions under the AI Act—but it is not one universal test. The European Commission’s AI Office uses documentation and training scale in its oversight of general-purpose AI (GPAI) providers, while Article 10 sets data-governance duties for high-risk AI systems that use model training. The AI Office is part of a shared enforcement framework with national authorities and the European Data Protection Supervisor (EDPS), not the sole AI regulator. European Commission: AI Act enforcement framework
Why training matters to the regulator
Training is evidence about how a model was developed, what material it learned from, and—where relevant—whether the data used for a high-risk system suit its intended purpose and operating context. Those facts can inform classification, provider obligations, and compliance assessment. The resulting duties depend on what is regulated: a GPAI model provider or a particular high-risk AI system.
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The Commission’s framework describes obligations and assessment inputs, not a finding that a named dataset or model has violated the Act. It is therefore more accurate to ask what training evidence a regulatory track requires than to ask whether training alone makes an AI system lawful or unlawful.
Two regulatory tracks, with different training questions
| Track | What is regulated | Training evidence that matters |
|---|---|---|
| GPAI provider obligations | The provider of a general-purpose AI model | Technical documentation, information for downstream providers, a copyright policy, a public summary of training content, and—in specified classification guidance—training compute and capability |
| High-risk AI system data duties | A high-risk AI system using techniques involving the training of AI models | Dataset provenance and preparation, suitability for intended purpose, representativeness, data gaps, and identification and mitigation of relevant bias |
These tracks should not be conflated: being GPAI and being a high-risk AI system are different regulatory questions. AI Act Service Desk: Article 10, data and data governance European Commission: Guidelines on obligations for general-purpose AI providers
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For high-risk systems, Article 10 focuses on dataset governance and fit
Article 10 applies to high-risk AI systems using techniques that involve training AI models. It requires data-governance and management practices appropriate to the system’s intended purpose. The law’s concern is not simply whether a dataset exists, but how it was selected and handled and whether it is suitable for the system’s use.
What governance must address
- Design choices and the processes used to collect data, including its origin.
- Where personal data are involved, the original purpose of their collection.
- Preparation steps such as annotation, labelling, cleaning, updating, enrichment, and aggregation.
- Assumptions about what the data are intended to measure or represent.
- Whether the data are available in sufficient quantity and suitable for the task.
- Potential bias that could affect health and safety, fundamental rights, or discrimination, and measures to detect and mitigate it.
- Material gaps or shortcomings in the data and how they are addressed.
Representativeness depends on the intended context
Article 10 says training, validation, and testing datasets must be relevant and sufficiently representative, and as free of errors and complete as possible in view of the intended purpose. What counts as a meaningful gap or representative sample can depend on the geographic, contextual, behavioural, or functional setting where the system is meant to operate. A dataset that appears adequate in one setting may not establish suitability for another.
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The provision does not promise that any dataset can be perfectly error-free. Its standard is tied to the intended purpose and expressed as “to the best extent possible”; that makes documenting data choices, limitations, and mitigation part of the compliance picture. Article 10 text and explanation
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For GPAI providers, training drives documentation and transparency duties
The Commission’s guidance says GPAI providers must keep technical documentation, provide information and documentation to downstream AI-system providers, establish a policy to comply with Union copyright law, and publish a sufficiently detailed summary of training content. These requirements make aspects of model development visible to authorities, downstream users, and the public, though they do not amount to a public release of the complete training dataset.
Providers of GPAI models with systemic risk face additional duties, including evaluation, risk assessment and mitigation, incident reporting, and cybersecurity safeguards. Training scale is one input to identifying models that may fall into that category; it is not a replacement for the wider assessment.
Does training compute decide whether a model is high risk?
No. The Commission’s GPAI guidance uses compute thresholds as indicators in GPAI and systemic-risk assessments; those thresholds do not decide whether a particular AI system is “high-risk” under the Act. The Commission describes an indicative criterion of more than 1023 floating-point operations (FLOP), combined with the ability to generate language, text-to-image, or text-to-video, for treating a model as GPAI. It describes 1025 FLOP as a threshold that creates a presumption of capabilities associated with systemic risk.
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These are Commission guidance, not permanent scientific definitions or automatic verdicts. The guidance allows for provider arguments, case-by-case Commission assessment, other routes to designation, and adjustment of thresholds as technology changes. It is non-binding; authoritative interpretation of EU law belongs to the Court of Justice of the European Union. Commission GPAI guidance and FAQ
Who makes and enforces the calls?
Enforcement is shared. The European Commission’s AI Office handles GPAI providers and specified connected systems; national competent authorities designated by EU Member States oversee other systems, and the EDPS covers systems used by EU institutions. This division matters because a question about a model provider’s GPAI duties may go to a different authority from a question about a high-risk system deployed in a member state.
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The Commission says the AI Office can request information, access GPAI models for evaluation, seek measures that may include restricting public availability, interview consenting people, and inspect provider premises in AI-system investigations. After establishing an intentional or negligent breach, the Commission may impose penalties. The stated maxima include up to €35 million or 7% of worldwide annual turnover for prohibited-practice infringements, and up to €15 million or 3% for other breaches, including GPAI obligations. These are legal ceilings, not predictions of typical penalties. European Commission: enforcement powers and penalty maxima
Application dates differ by obligation
The AI Act’s requirements are phased rather than activated by one universal start date. According to the Commission’s enforcement overview, GPAI provider obligations entered into application on 2 August 2025. Specified enforcement powers apply from 2 August 2026; Annex III high-risk system rules are scheduled for 2 December 2027, and rules for high-risk systems embedded in regulated products are scheduled for 2 August 2028. These dates describe the Commission’s stated timetable and may be affected by subsequent legal or implementation changes. European Commission: AI Act enforcement timetable
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