Model card

A model card is a structured document that summarizes what an artificial intelligence or machine learning model is, what it was designed to do, how it was evaluated, and what limits or risks should be understood before use. It commonly refers to a human-readable description that travels with the model or is linked to it in a repository, application, or governance workflow.

In industrial and regulated environments, a model card is typically used as supporting documentation for transparency and internal review. It can help teams understand the model’s purpose, input and output expectations, training or reference data characteristics at a high level, performance measures, known constraints, and operational assumptions. It is documentation about the model, not the model itself.

What it usually includes

• The model’s name, version, and owner or maintaining team

• Intended use cases and users

• Out-of-scope or prohibited uses

• Input data expectations and output format

• Summary of how the model was trained or configured

• Evaluation approach and reported performance metrics

• Known limitations, failure modes, or bias considerations

• Operational dependencies such as data quality, thresholds, or human review requirements

How it appears in operations

Model cards often appear in AI governance records, MLOps repositories, validation packages, supplier documentation, or approval workflows tied to analytics and decision-support tools. For example, a manufacturer using a machine learning model for visual inspection or maintenance prediction may keep a model card alongside version-controlled deployment records so quality, engineering, and IT stakeholders can review the model’s stated purpose and limits.

Common confusion

A model card is often confused with related artifacts, but they are not the same:

• Data sheet or dataset documentation: describes the dataset rather than the model.

• System documentation: covers the broader application, workflow, or architecture, not just the model.

• Validation report: provides evidence from testing or qualification activities, while a model card is a summary-oriented description.

• Algorithm specification: may describe logic or mathematics in depth, whereas a model card is usually broader and more operational.

Boundary of the term

The term commonly refers to documentation for AI or machine learning models, including predictive, classification, detection, or generative models. It does not by itself imply regulatory approval, production readiness, cybersecurity assurance, or fitness for a specific quality-critical decision. Those determinations depend on the surrounding governance, validation, and operational controls.