magnus919_agent-skills/ai-governance/templates/model-card.md

Model Card

Confidentiality: A completed model card records intended use, data, performance, fairness results, limitations, and monitoring for a specific model. Store it with access controls appropriate to the sensitivity of the model and its data. This template implements the model-card documentation discipline described in references/fairness-bias-accountability.md and references/ai-lifecycle-governance.md, and is informed by the current state of model and data cards in research-technical-controls.md. It is the durable, audit-ready record that accompanies a model-registry entry.

When To Use

Use this card to document any model you develop, procure, or operate so that it is understood and applied only where it is suited. Finalize it at the evaluation gate, before deployment, and refresh it when the model, its data, its context of use, or its performance changes materially. The card is both the evidence that clears the evaluation gate and the reference that the monitoring and incident-response phases consult later. For a point-in-time analysis of how risky a model is and which tier it belongs in, use model-risk-assessment.md.

When Not To Use

Do not use this card as a substitute for a risk assessment and tiering decision (model-risk-assessment.md), and do not use it in place of a use-case intake form for a brand-new application (use-case-intake-form.md). The card documents what a model is and how it behaves; it does not decide whether the residual risk is acceptable. Keep the card current through the lifecycle rather than treating it as a one-time launch artifact.

Model Identity And Overview

Field	Entry
Model name and version
Model type	<e.g. classifier, regressor, LLM, agentic system, recommender>
Model registry / inventory ID
Owner / accountable role
Development team
Release / card version
Effective date
Next review date

Intended Use

Describe what the model is for, who it serves, and where it should and should not be applied, so it is not used in a context for which it is unsuited.

• Intended function: <what the model does, e.g. classify, recommend, generate, automate, decide>
• Intended users and audience:
• Intended domain and context of use: <the specific business process, environment, and jurisdiction>
• Intended decision outcome:
• Known non-intended uses:
• Deployment status: <development / evaluation / production / shadow / retired>

Data Description

Document what the model was trained on and what it consumes in production, so its provenance and limitations are explicit.

• Training data sources: <data sets, systems, or vendors supplying training data>
• Data collection and annotation method:
• Data sensitivity: <public / internal / confidential / personal / sensitive personal / regulated>
• Contains personal or protected-attribute data: <yes/no — if yes, list the types>
• Data lineage and governance:
• Known data quality issues and gaps: <errors, missingness, historical bias, or staleness>

Performance And Evaluation

Record how the model was evaluated, on what data, and how it performed — broken out by relevant subgroups so the spread across groups is visible rather than being collapsed into one average.

• Evaluation procedure: <test sets, holdouts, cross-validation, or benchmark methodology>
• Primary performance metrics and thresholds: <e.g. accuracy, precision, recall, F1, calibration, quality>
• Overall results:
• Results by subgroup:
• Robustness and edge cases:
• Evaluation limitations:

Fairness And Bias Assessment

State which fairness metrics were chosen, why, what they showed across groups, and which decisions were made in response — not a single number but an explicit, auditable statement.

• Fairness metrics computed: <e.g. demographic parity, equalized odds, equal opportunity, calibration>
• Metric selection rationale:
• Results across protected groups: <what the subgroup results showed, including any disparate impact>
• Known biases or proxy features:
• Trade-offs accepted: <what was given up in accuracy or parity, and why it was defensible>
• Mitigations applied: <reweighting, threshold adjustment, human review, or other corrective actions>

Limitations And Considerations

Document the situations where the model is likely to underperform, so it is not applied somewhere it is unsuited.

• Known limitations: <where the model is likely to fail, mislead, or behave unpredictably>
• Suitable versus unsuitable contexts:
• Environmental or dependence assumptions:
• Security and integrity concerns: <prompt injection, misuse, data exposure, supply chain, tooling>
• Human oversight requirements:

Monitoring And Maintenance

Define how the model is watched in production and how it is kept current, so that drift, degradation, or reintroduced bias is detected and responded to rather than improvised.

• Monitoring configuration: <metrics, alert thresholds, owners, and response runbook>
• Drift detection approach:
• Retraining / refresh cadence:
• Incident and response path:
• Retirement plan:

Approval And Review

Record who approved the card and its contents, and how it is kept current.

Review field	Entry
Prepared by
Reviewed by
Approved by
Approval date
Review trigger	<annual / on drift / on material change / on incident>

Completion

To complete this card: fill every labeled field, finalize the intended-use and non-intended-use statements, record the data description and its limitations, report performance broken out by subgroup rather than a single average, state the fairness metrics chosen and their results, document limitations and human oversight requirements, define the monitoring and drift-detection configuration, and obtain the approvals named above. Attach the card to the model-registry entry as the source-of-record, and refresh it whenever the model, its data, its context of use, or its performance changes materially or on the review trigger you set.

Synthesized from research-technical-controls.md and research-org-board-governance.md and the ideas of Responsible AI in the Enterprise, Responsible AI: Best Practices, and AI Fairness (see references/fairness-bias-accountability.md and references/ai-lifecycle-governance.md). Fillable artifact of the ai-governance skill.