AI Model Card Template
Download an AI model card template covering model details, intended use, training data, evaluation, performance metrics, ethical considerations, limitations, and recommendations.
Create a comprehensive model card in 45-90 minutes.
For compliance, risk, product, and ML ops teams shipping agentic workflows into regulated environments.
Last updated: Dec 16, 2025 · Version v1.0 · Fictional sample. Not legal advice.
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What this artifact is (and when you need it)
Minimum viable explanation, written for audits — not for theory.
Model cards are standardized documentation for AI models. Originally proposed by researchers at Google in 2019, they have become an industry best practice for communicating what a model does, how it performs, and what its limitations are.
For organizations subject to the EU AI Act, model cards help satisfy transparency requirements under Article 13 and contribute to the technical documentation required by Annex IV.
You need it when
- You are deploying ML models and need standardized documentation for technical reviewers.
- You need to satisfy EU AI Act transparency requirements (Article 13) or contribute to Annex IV documentation.
- You want to communicate model capabilities, limitations, and ethical considerations to deployers and users.
Common failure mode
Model cards that only report favorable metrics, omit known limitations, or lack disaggregated performance across relevant subgroups and conditions.
What good looks like
Acceptance criteria reviewers actually check.
- Model identification includes version, architecture, lineage, and development provenance.
- Intended use defines primary use cases, intended users, and explicitly states out-of-scope uses.
- Training data is documented with sources, characteristics, preprocessing, and known quality issues.
- Performance metrics include overall and disaggregated results with confidence intervals.
- Ethical considerations cover fairness metrics, bias testing, and protected characteristics.
- Limitations are comprehensively documented including failure modes and boundary conditions.
- Recommendations provide actionable deployment, monitoring, and maintenance guidance.
Template preview
A real excerpt in HTML so it’s indexable and reviewable.
## Section 2: Intended Use ### 2.1 Primary Use Cases | Use Case | Description | User Type | |----------|-------------|-----------| | [Primary use case 1] | [Detailed description] | [Who uses it] | ### 2.3 Out-of-Scope Uses | Use Case | Reason Not Supported | |----------|---------------------| | [Prohibited use case 1] | [Why inappropriate: data limitations, ethical concerns, etc.] | ## Section 7: Limitations ### 7.2 Known Failure Modes | Failure Mode | Trigger Conditions | Detection | Response | |--------------|-------------------|-----------|----------| | [Failure mode 1] | [What causes this failure] | [How to detect] | [What to do] |
How to fill it in (fast)
Inputs you need, time to complete, and a miniature worked example.
Inputs you need
- Model architecture, version, and training provenance details.
- Intended use cases and explicit out-of-scope uses.
- Training and evaluation data documentation with known issues.
- Performance metrics (overall + disaggregated) with confidence intervals.
- Fairness analysis results and ethical considerations.
- Known limitations, failure modes, and boundary conditions.
Time to complete: 45-90 minutes for a comprehensive v1.
Mini example: out-of-scope use
Out-of-Scope Uses: | Use Case | Reason Not Supported | |----------|---------------------| | Sole decision-making for credit | Model designed as decision support only; requires human review | | Use on populations not in training data | Performance not validated; may produce unreliable results | | Real-time safety-critical applications | Latency requirements not validated for this use case |
How KLA generates it (Govern / Measure / Prove)
Tie the artifact to product primitives so it converts.
Govern
- Version-controlled model cards linked to model registry and deployment pipelines.
- Approval gates that verify model card completeness before production deployment.
Measure
- Automated capture of performance metrics, drift signals, and fairness indicators.
- Disaggregated performance monitoring across protected groups and edge conditions.
Prove
- Model cards linked to evaluation artifacts, training data lineage, and approval records.
- Evidence bundles that tie documentation claims to runtime performance data.
FAQs
Written to win snippet-style answers.