Model Card

Key Takeaway: A model card is a standardized documentation artifact that describes an AI model's purpose, capabilities, limitations, training data, performance metrics, and ethical considerations. Under the EU AI Act, equivalent documentation is legally required for high-risk AI systems — and enterprise buyers should demand model cards from every AI vendor they evaluate.

What Is a Model Card?

A model card is a short document that accompanies an AI or machine learning model, providing structured, standardized information about the model's design, training, performance, intended use cases, and known limitations. The concept was introduced by Google researchers (Mitchell et al., 2019) and has since become a widely adopted practice in the AI research and industry community.

Think of a model card as the nutritional label on packaged food, or the safety data sheet for a chemical product — a standardized disclosure that allows users to make informed decisions about whether and how to use the model. Without a model card (or equivalent documentation), users are making decisions about AI adoption with incomplete information.

Model cards are directly relevant to regulatory compliance: the [link:/glossary/ai-act]'s Article 11 requires that providers of [link:/glossary/high-risk-ai-systems] maintain technical documentation covering much of the same ground as a model card. The GPAI model documentation requirements under Articles 53–55 require foundation model providers to publish technical documentation about their models' training data, capabilities, and limitations. [link:/glossary/trustworthy-ai] frameworks and [link:/glossary/iso-42001] governance standards also require this type of documentation as a condition of responsible AI management.

What a Model Card Contains

A complete model card typically includes the following sections:

Model details: Basic information about the model — who developed it, when, what version, what type of model (classification, generation, ranking, etc.), and what framework it was built with.

Intended use: The specific tasks and populations the model was designed for, and explicitly what it was NOT designed for. This is critical for deployers: using an AI model outside its intended use case is both a compliance risk and a performance risk.

Training data: Description of the datasets used to train the model — their sources, size, collection methods, and any known biases or limitations. This section directly supports [link:/glossary/algorithmic-bias] assessment.

Evaluation results: Performance metrics across different test sets and demographic subgroups. A model card that only reports aggregate accuracy without disaggregated performance data is incomplete for fairness purposes.

Ethical considerations: Known risks, biases, misuse scenarios, and the mitigations the model developer has applied. This section answers the "what could go wrong?" question.

Caveats and recommendations: Guidance on appropriate use, limitations, and conditions under which the model should not be used.

Quantitative analyses: Disaggregated performance metrics across groups — gender, age, geography, language — to enable [link:/glossary/ai-fairness] assessment.

Why It Matters for Business

Due diligence baseline: Enterprise AI procurement should require model cards (or equivalent technical documentation) from every AI vendor. A vendor that cannot or will not provide model documentation is a vendor that cannot satisfy your deployer obligations under the EU AI Act.

Risk assessment input: The model card's intended use section immediately reveals whether an AI system is being used outside its designed parameters — a common source of compliance risk and performance degradation. The evaluation results enable independent assessment of whether the model is sufficiently accurate for the deployment context.

Bias and fairness evidence: Disaggregated performance metrics in a model card are the primary evidence base for assessing whether an AI system is likely to produce discriminatory outcomes. An [link:/glossary/ai-impact-assessment] cannot be conducted meaningfully without this data.

Regulatory documentation: For high-risk AI systems under the EU AI Act, the technical documentation requirement (Article 11) overlaps substantially with model card content. Organizations that maintain model cards for their own AI systems or require them from vendors have a head start on this compliance obligation.

Foundation model requirements: Providers of general-purpose AI models (including large language models used via API) are required under Articles 53–55 of the EU AI Act to publish technical documentation and summaries of training data. Model cards are the established mechanism for this disclosure. See [link:/glossary/foundation-model-regulation].

Compliance Checklist: Model Cards

Is a model card (or equivalent technical documentation) required from all AI vendors as part of procurement?
Does the model card include disaggregated performance metrics across demographic subgroups?
Is the intended use section reviewed to confirm the deployment is within scope?
Are model cards reviewed and updated when models are retrained or updated?
For internally developed AI: is a model card produced as a standard development artifact?
Are model cards stored and accessible for regulatory audit purposes?
For GPAI model providers: is technical documentation published as required by Articles 53–55?

Related Terms

[link:/glossary/ai-act]
[link:/glossary/ai-transparency]
[link:/glossary/algorithmic-bias]
[link:/glossary/ai-fairness]
[link:/glossary/foundation-model-regulation]
[link:/glossary/ai-audit]

How Knowlee Addresses Model Cards

Knowlee produces and maintains internal technical documentation for its AI models that covers the core content of a model card — training data sources, performance metrics, intended use cases, known limitations, and bias testing results. This documentation is made available to enterprise customers as part of the technical transparency required by the EU AI Act's Article 26 deployer obligations.

Knowlee's explainable output design is the operational complement to model card documentation: while the model card describes the system at the design level, Knowlee's in-platform explanations surface the model's reasoning at the decision level — giving users both the structural context (what the model is designed to do) and the specific context (why it made this particular recommendation) needed for informed oversight.