AI Model Card Template

Documentation standard for individual AI models: identity, purpose, training data, performance metrics, fairness testing, limitations, monitoring, governance approvals, version history.

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Key Insights

Model cards document what an AI system is, how it works, and what its limitations are—providing transparency for governance, compliance, and stakeholder communication. As AI regulations require greater documentation (EU AI Act high-risk system requirements, state transparency laws), model cards become compliance necessities, not just best practices.

This template provides comprehensive model documentation: identity and purpose, training data details, performance metrics overall and by subgroup, fairness analysis, limitations and risks, human oversight requirements, and version history.

Overview

What does this AI model do? What data was it trained on? How well does it perform? Are there known limitations? Where might it fail? These questions need documented answers—for governance review, regulatory compliance, and stakeholder transparency.

Model cards provide that documentation in a standardized format. This template captures everything stakeholders need to know about an AI model.

What's Inside

Model Identity

Model name, ID, version
Model type and framework
Development team/vendor
Deployment date and status

Model Overview

Purpose and use case description
Intended users
Out-of-scope uses (explicitly prohibited applications)

Training Data

Data sources and collection methods
Data volume and time period
Data types and PII indicators
Data preprocessing applied
Known biases in training data
Data refresh frequency

Performance Metrics

Overall performance table (accuracy, precision, recall, F1, AUC-ROC)
Training, validation, and production metrics
Custom business metrics

Fairness Analysis

Performance across subgroups
Disparate impact analysis
Fairness testing methodology and results
Known disparities and mitigations

Limitations & Risks

Known limitations
Failure scenarios
Risk classification
Mitigation measures in place

Human Oversight

Automation level (human-in-the-loop, human-on-the-loop, autonomous)
Override capabilities
Escalation procedures
Monitoring and intervention points

Explainability

Explainability level (high, medium, low, none)
Available explanation methods
Explanation examples

Version History

Version changelog
Material changes documentation

Approvals

Model owner, risk/compliance, technical approval signatures
Approval dates

Who This Is For

AI Teams documenting models for governance
Risk/Compliance reviewing model documentation
AI Governance maintaining model registry
Auditors examining AI documentation
Regulators assessing transparency compliance

Why This Resource

Standardized documentation enables consistent review across your AI portfolio. This template captures what governance needs—not just technical details, but use cases, limitations, fairness analysis, and oversight requirements.

The format aligns with emerging regulatory expectations for AI documentation.

FAQ

Q: Is this required for regulatory compliance?

A: EU AI Act requires documentation for high-risk AI systems. Some US state laws require transparency documentation. Even where not legally required, model cards are governance best practice.

Q: Who should complete model cards?

A: AI/ML teams complete technical sections; business owners complete use case and oversight sections; risk/compliance reviews and approves. The template includes approval signatures.

Q: How often should model cards be updated?

A: Update when models are retrained, when performance changes significantly, when use cases change, or when limitations are discovered. The version history section tracks changes.