Model Card

Model cards make critical information about a model visible to the people deciding whether to deploy it. They help procurement, compliance, and engineering teams understand a model's strengths, weaknesses, and recommended uses before integration.

Model cards typically include:

• Model details: Name, version, owner, training date, and intended domain.
• Intended and out-of-scope uses: Allowed use cases and explicit cautions.
• Training data: Sources, scale, and known biases of the underlying data.
• Performance metrics: Evaluation results across relevant tasks and demographics.
• Limitations and ethics: Known failure modes and ethical considerations.

In practice, model cards are most useful when they are kept current with deployment context and tied to incident history. Stale model cards leave organizations vulnerable to drift between what was promised at procurement and what is happening in production.

Programs that mature fastest treat model card maintenance as part of the model release process and require updates whenever evaluation results or deployment scope change.

How PointGuard AI Helps

PointGuard AI Discovery surfaces models in use along with their available model cards, and AI Governance enforces requirements for documentation completeness as part of approval workflows. The combined view makes it straightforward to confirm that every production model has up-to-date documentation before approval.

Learn More

• Mitchell et al., Model Cards for Model Reporting arXiv:1810.03993
• NIST AI Risk Management Framework
• Hugging Face: Model Cards Documentation