An AI model card should disclose four things plainly: which safety evaluations were run, how they were run, what the results mean, and what remains unknown or limited. A useful card identifies the model version and use context, describes protocols and metrics, separates model testing from red-teaming and field testing, reports findings with test conditions, and explains changes made after testing. For general-purpose AI (GPAI) models with systemic risk, the European Union’s AI Act Annex XI provides a detailed benchmark, but a public model card is only a summary and may not replace technical documentation required for authorities or downstream providers.
Start with the model and the safety question
Safety results are interpretable only when readers know exactly what was evaluated. Begin with a stable model identifier, release or version, evaluation date, intended tasks, modalities, and the use contexts covered. If a provider has several checkpoints, fine-tuned variants, or deployment configurations, identify which one produced the reported results.
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Identify the risks in scope
State the safety properties and harms the evaluation addressed. Examples might include harmful-content refusal, privacy leakage, cybersecurity misuse, bias, robustness to prompt attacks, or risks associated with a particular deployment. Do not use a single word such as “safe” as a substitute for this scope. A model can perform well on one property while remaining untested or weak on another.
State what was not evaluated
List material risks, languages, modalities, user groups, environments, and attack types that were outside the evaluation. An explicit “not evaluated” statement prevents readers from treating a narrow test as evidence of general safety.
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Describe the evaluation method, not just a label
A score or badge is useful only when a reader can understand how it was produced. The card should name the evaluation strategy, protocols, tools, datasets or scenarios where appropriate, test conditions, and the model version used. For GPAI models with systemic risk, Annex XI calls for “A detailed description of the evaluation strategies, including evaluation results, on the basis of available public evaluation protocols and tools or otherwise of other evaluation methodologies.”
Document the protocol
Explain how prompts, scenarios, samples, annotators, and pass or fail rules were selected. If the provider used a public benchmark, identify its edition and any modifications. If it created an internal test, describe the construction and sampling process sufficiently for a qualified reader to understand what the test represents.
Define the measures
For every metric, give its definition, unit, denominator, threshold, and test conditions. “Refusal rate,” for example, can mean different things depending on whether safe answers, partial compliance, or incorrect refusals count as failures. Include confidence intervals or uncertainty information when the provider has calculated them, and identify small samples or exploratory results as such.
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Present the observed result first, then explain the interpretation and its limits. A high score on a curated set of prompts does not establish safe behavior in an open-ended product. Do not imply that passing a benchmark proves the absence of a risk that the benchmark did not measure.
Use three testing levels so coverage is clear
NIST’s ARIA materials distinguish model testing, red-teaming, and field testing. These levels answer different questions and should appear as separate headings or clearly labeled subsections in the card.
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| Testing level | Primary question | What to disclose | What it cannot establish alone |
|---|---|---|---|
| Model testing | How does the model behave on defined tests under controlled conditions? | Benchmarks or scenarios, prompts, sampling, metrics, thresholds, version, and results. | How the model will behave in a particular product, user population, or operational environment. |
| Red-teaming | Can deliberate adversaries elicit harmful, deceptive, unsafe, or otherwise unwanted behavior? | Who tested, attack areas, access and tools, important findings, severity criteria, and mitigations. | That all attack paths or future threats have been found. |
| Field testing | What happens in a real or representative deployment with users, workflows, and safeguards? | Deployment context, user population, monitoring, incident criteria, duration, and observed events. | That results transfer unchanged to another product, region, or configuration. |
A model-level evaluation does not by itself establish performance in a specific deployment. Conversely, field observations may reveal operational failures that a controlled benchmark misses. The card should identify which evidence exists rather than combining all results into one undifferentiated safety claim.
Report adversarial testing and red-team work
Where applicable, identify internal and external adversarial testing. Describe the areas attacked, such as jailbreaks, prompt injection, data extraction, unsafe tool use, manipulation, or model autonomy, without publishing details that would create an avoidable security risk. Summarize the test design, access level, number and type of testers, important findings, and the criteria used to judge severity.
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Explain practical disclosure limits
A card can withhold exploit strings, personal data, or operational details that would materially increase abuse, but it should say that such details were withheld and still report the nature and significance of the finding. “Red-teamed” without scope, date, or result is not meaningful disclosure.
Connect findings to mitigations
For significant findings, state whether the provider changed the model, system prompts, filters, tools, access controls, monitoring, or user experience. Identify whether the reported test result is from the pre-mitigation or post-mitigation version. If a mitigation was tested only against the original attack, say whether broader regression testing was performed.
Show results and limitations together
Every results section should have a corresponding limitations section. Annex XI expressly addresses evaluation criteria, metrics, and the methodology used to identify limitations. A practical card should turn that requirement into readable evidence rather than a generic disclaimer.
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Present findings with context
- Give the result, metric definition, sample or scenario, test date, and model version in the same table or paragraph.
- Distinguish pass, fail, mixed, and inconclusive outcomes.
- Explain important false positives, false negatives, and known failure modes.
- Identify language, modality, tool, and context restrictions.
- State whether evaluators were independent, internal, or both.
Describe how limitations were found
Explain whether limitations came from benchmark analysis, red-team discoveries, user reports, incident reviews, monitoring, or another method. If the provider has not established a reliable method for a particular limitation, say so instead of implying comprehensive coverage.
Keep uncertainty visible
Do not convert “no failures observed” into “no failures exist.” A test may have limited coverage, a small sample, known annotation disagreement, or conditions that differ from production. Those qualifications belong beside the result, not in a distant footnote.
Document adaptations made after testing
Safety testing often changes a model or its surrounding system. The card should record relevant adaptations such as alignment training, safety fine-tuning, preference optimization, refusal-policy changes, tool restrictions, or monitoring updates. Give the change date or release and identify the tests run after the change.
Make version boundaries explicit
If a red team tested version 1.2 but the public model is version 1.3, label the results accordingly. Do not present pre-change failures as current performance or present post-change results as evidence about an earlier release. When a mitigation is still being evaluated, mark it as an open item.
Include regression testing
Safety changes can create new weaknesses, over-refusal, or degraded performance on legitimate requests. Report material regression tests and their outcomes when available. If no regression evaluation was performed, state that limitation.
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Write for the right audience and legal scope
The EU AI Act provides a concrete benchmark, not a universal model-card standard. The Commission distinguishes technical documentation made available to authorities on request from information supplied to downstream AI-system providers so they can understand a model’s capabilities and limitations and meet their own obligations.
A public card can summarize relevant facts for users, developers, researchers, and the public. It should not imply that the summary alone satisfies every provider’s legal duty. Whether a particular disclosure is sufficient depends on the provider, model classification, audience, deployment, and applicable law.
Systemic-risk GPAI disclosures
For GPAI models with systemic risk, the Commission’s materials identify evaluation using standardized protocols and state-of-the-art tools, documented adversarial testing to identify and mitigate systemic risks, systemic-risk assessment and mitigation, serious-incident tracking and reporting, and cybersecurity protection. A card should point readers to the applicable technical documentation or compliance channel when those materials are maintained separately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A disclosure checklist readers can audit
- Model identification: name, version or release, date, modalities, intended tasks, and relevant use context.
- Evaluation scope: safety properties, risks, populations, languages, modalities, and deployment conditions tested.
- Strategy and protocol: internal or external design, datasets or scenarios, tools, sampling, prompts, and pass criteria.
- Metrics and conditions: definitions, thresholds, denominators, uncertainty, and the conditions under which each metric was measured.
- Results: findings that distinguish successful, failed, mixed, and inconclusive outcomes.
- Adversarial testing: red-team scope, tester type, attack areas, significant findings, and practical limits on disclosure.
- Changes after testing: alignment or fine-tuning, system-level mitigations, release boundaries, and post-change tests.
- Limitations: known failure modes, untested areas, transfer limits, and the method used to identify gaps.
- Follow-up: monitoring, incident handling, or planned updates only when the provider actually operates them.
How to compare two model cards fairly
Comparisons are valid only when the evidence is comparable. Use the same axes for each model:
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|---|---|
| Risks and context | Were the same harms, users, languages, modalities, and deployment settings evaluated? |
| Testing level | Is the evidence model testing, red-teaming, field testing, or a combination? |
| Protocol and tools | Were the scenarios, benchmarks, attack methods, and tool access equivalent? |
| Metrics and conditions | Do the metric definitions, thresholds, samples, and test dates match? |
| Findings and limitations | Are failures, uncertainty, and untested areas reported with equal candor? |
| Independence | Were tests internal, external, or both, and who had access to the model? |
| Version history | Were changes made after testing, and are results tied to the same release? |
A larger score is not automatically stronger evidence. A narrowly defined external test and a broad internal test may measure different properties; the card should let readers see that difference.
Common disclosure failures and how to fix them
“The model is safe” with no evidence
Replace the label with named risks, methods, results, and limitations. State what the evidence supports and what it does not.
A benchmark score without a definition
Add the metric formula or operational definition, test conditions, sample size or scenario set, and threshold. Explain known sources of uncertainty.
“Red-teamed” as a marketing adjective
Identify the testers, access, attack areas, significant findings, and mitigations. Protect sensitive exploit details without hiding the result.
Mixing versions
Attach every result to a release identifier and explain adaptations between tested and deployed versions.
Ignoring deployment conditions
Separate controlled model results from field evidence. Describe system prompts, retrieval, tools, rate limits, human review, and monitoring when they materially affect safety.
Using one disclaimer for every limitation
List concrete failure modes and untested areas. A generic statement that “AI may make mistakes” does not tell a downstream provider what controls are needed.
What a strong model card lets a reader decide
After reading the safety section, a developer should be able to determine whether the evidence matches the intended use, which safeguards remain necessary, and where independent testing is warranted. A regulator or auditor should be able to trace claims to methods, metrics, versions, and findings. A user should be able to understand the important limitations without needing to infer them from a score.
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