AI Model Card Template (EU AI Act, Article 11 / Annex IV)

A model card is the one-page summary of what a model is, what it was trained on, how well it performs, and where it breaks. It is not a statutory term — you will not find "model card" in the EU AI Act. But it is the single most useful practice for populating the documentation the Act does require: the Article 11 technical documentation, whose contents are spelled out in Annex IV, and, for general-purpose AI models, the downstream information a provider must supply under Article 53. This template gives you the fields, organised so they map straight onto those obligations.

Treat the model card as the readable front end of your technical documentation, not a substitute for it. Annex IV asks for nine areas of detail; a good model card captures the core of each in a form your own engineers, your deployers, and an auditor can all read. It is the provider's artefact — Article 11 sits with whoever develops the high-risk system and places it on the market.

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What the documentation has to cover (Annex IV)

Article 11 requires providers of high-risk systems to draw up technical documentation before the system is placed on the market, and to keep it current. Annex IV lists what goes in it — among the nine areas: a general description of the system and its intended purpose; the elements of its development, including design specifications and the data used; the system's capabilities and limitations, including its expected level of accuracy for specific persons or groups; the human-oversight measures; and the validation and testing procedures. A model card is how you make those legible.

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The Article 15 performance numbers

Vague claims fail an audit. Article 15 requires high-risk systems to achieve an appropriate level of accuracy, robustness and cybersecurity, and to declare the relevant accuracy metrics in the Article 13 instructions for use. The model card is where you record the numbers behind those declarations: the metric, the value, the test set it was measured on, and — crucially — performance broken down for specific persons or groups where that matters (Annex IV asks for exactly this). A single headline accuracy figure hides the disparities that bias and oversight reviews then have to surface.

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The GPAI angle (Article 53)

If the model is a general-purpose AI model, a second documentation duty applies. Article 53 requires GPAI providers to draw up and keep technical documentation of the model, and to make information and documentation available to downstream providers who integrate it — so they can understand the model's capabilities and limitations and meet their own obligations. Article 53 also covers a copyright policy and a sufficiently detailed summary of the training-data content. A model card is a natural vehicle for the downstream-information part: it is precisely the "what you need to know to build on this safely" document Article 53 has in mind. (Note the timing: GPAI obligations under Chapter V are already in application from 2 August 2025 — they were not deferred by the Digital Omnibus.)

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The template — fields to capture

1. Model identification — name, version, provider, release date; whether it is a stand-alone high-risk system, an Annex I product component, or a general-purpose AI model.

2. Intended purpose and out-of-scope uses — the intended purpose as it will appear in the Article 13 instructions; explicitly listed uses the model is not validated for.

3. Training-data overview — sources, scale, time span, languages/domains; preprocessing; representativeness notes (link to the Article 10 data-governance and bias records).

4. Performance and accuracy (Article 15) — metrics, values, the test set used; per-group breakdown where relevant; robustness and cybersecurity test results.

5. Limitations and failure modes — where the model underperforms; foreseeable misuse; known edge cases.

6. Human oversight (Article 14) — the oversight mechanisms designed in; how an overseer interprets and can override outputs.

7. Annex IV cross-reference — which Annex IV section each field populates, so the card and the full technical documentation stay in sync.

8. GPAI downstream information (Article 53) — for general-purpose models: capabilities, limitations, integration guidance, the training-data summary reference, and the copyright-policy reference.

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Worked example: a fintech provider's scoring model

Kreditlux SARL, a provider with about 90 employees and roughly €22M turnover, develops a high-risk creditworthiness model (Annex III point 5(b)). Its model card states the intended purpose — scoring consumer loan applications in three EU markets — and lists out-of-scope uses (no SME lending, no insurance pricing). The performance section reports Article 15 accuracy by market and by applicant segment, exposing weaker performance on thin-file applicants, which it flags as a limitation. The human-oversight section documents the Article 14 override available to credit analysts. Every field is tagged to its Annex IV section, so the card and the full Article 11 documentation never drift apart. Kreditlux's model is not a general-purpose AI model, so the Article 53 GPAI section is marked not-applicable — recorded, not silently omitted.

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How Confir helps

Confir's data-and-technical-robustness and transparency-and-oversight assessments capture the intended purpose, Article 15 performance figures, limitations and Article 14 oversight design as structured fields, then assemble them into the Article 11 / Annex IV technical-documentation pack — so the model card is a view onto the same record an auditor sees, not a separate document that goes stale. Where the model is general-purpose, it prompts for the Article 53 downstream information. The engine is deterministic and rule-based.

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Related guides

• Article 11 technical documentation
• Annex IV technical documentation explained
• Annex IV technical documentation template
• Article 15 accuracy, robustness and cybersecurity
• Technical documentation obligations
• GPAI provider obligations