Model Card

Data artifactGovernance & ComplianceSafety, Security & Governancearc:ModelCard

A transparency document describing a model's intended use, training data, performance and fairness metrics by group, known limitations, and the rationale for features that passed fairness review.

Responsibility. Documents a model's intended use, limitations, and fairness evaluation.

Also known as: Transparency documentation, Limitations documentation, Feature fairness review documentation, Technical documentation (EU AI Act), Implementation documentation, Model documentation, Nutrition label for AI

Direct neighbourhood (hover for relationship types)

Relationships

is read by dependency

Stage Promotion Controller Ref9.02 Ref9.03

sends data to dynamic

is audited by assurance

is evaluated by assurance

AI Documentation Reviewer Ch9.8

is produced by lifecycle

Model Card Generator Ch9.8

Design guidance

SHOULD document why potentially problematic features passed fairness review, enabling later challenge and revision.
SHOULD disclose limitations and risks clearly to stakeholders.
MUST document training and test data, annotation processes, algorithm logic, performance and bias metrics, limitations, failure modes and human oversight mechanisms for high-risk systems.
SHOULD describe the guardrails, constraints and feedback mechanisms through which values are embedded.
MUST document limitations, known biases and failures explicitly rather than marketing strengths.
SHOULD report performance disaggregated by demographic group and under distribution shift and adversarial testing.
SHOULD be concise but complete, pointing to detailed technical documentation ('nutrition label, not biochemistry textbook').
SHOULD maintain version history with reasons for updates and performance changes between versions.

Quantitative guidance

As stated by the sources; verify before use.

Documentation for production systems retained 5-7 years; 3 years post-decommission (Ref9.03).

Classification

Patterns: Model Cards for Model Reporting
Quality attributes: Transparency and accountability (NIST AI RMF: accountable and transparent)Interaction capability (ISO/IEC 25010)
Risks mitigated: Misapplication of models outside intended useHidden biases and failure modesLiability from undocumented known weaknesses
Frameworks & regulations: EU AI Act (high-risk requirements)EU AI Act: technical documentation / general-purpose model documentationISO/IEC 42001 Annex A: transparency controlsNIST AI RMF: MAP

Sources

Ch9.4: T. Nguyen, "Fairness and Bias Mitigation," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.4. ISBN: 9798244538229.
Ch9.6: T. Nguyen, "Value Alignment Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.6. ISBN: 9798244538229.
Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
Ref9.02: "Responsible AI and Ethical Principles," unpublished reference note (02-Responsible-AI-Ethical-Principles.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
Ref9.03: "Regulatory Compliance Frameworks for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/03-Regulatory-Compliance-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note