Auto-Generated Model Cards

Model cards are standardized documentation of AI systems, including what they do, how they were trained, how they perform, and their limitations. EU AI Act Annex IV and ISO 42001 require them for high-risk systems. Compliance AI auto-generates comprehensive model cards from your system profile in 2-3 minutes.

What Is a Model Card?

A model card is a 2-10 page document describing an AI system. Think of it like a product specification sheet for ML models. It helps regulators, auditors, users, and developers understand what the system does and doesn’t do.

Standard sections:

1. System Overview
2. Intended Use & Users
3. Training Data & Limitations
4. Performance & Fairness
5. Safety & Security
6. Monitoring & Maintenance

How Compliance AI Generates Model Cards

Step 1: Auto-Generate (2-3 minutes)

1. Go to Registry > [System Name] > Model Card
2. Click Generate Model Card
3. Compliance AI auto-fills sections from:
   • System profile (name, type, description)
   • Auto-discovery data (deployment location, data sources)
   • Risk assessment (identified risks)
   • Infrastructure connectors (performance metrics, logs)
4. Generates draft PDF or DOCX

Sections auto-populated:

• System identification and versioning
• Intended use and users
• Known limitations
• Deployment information

Sections flagged for human review:

• Training data description (requires data governance details)
• Performance metrics (requires actual test results)
• Fairness/bias assessment (requires bias testing)
• Known risks and mitigation

Step 2: Review & Customize (15-30 min)

Edit sections requiring human input:

Section	What to Add
Training Data	Where did data come from? How much? What are characteristics?
Performance Metrics	Test accuracy, precision, recall, F1-score, latency
Fairness Assessment	Demographic parity, disparate impact, group performance gaps
Failure Modes	When/how does system fail?
Use Restrictions	What is this NOT meant to do?
Recommendations	Best practices for deployment and monitoring

Compliance AI learns from edits and improves future model cards.

Step 3: Export & Archive

Export for compliance records:

1. Click Export
2. Select format:
   • PDF — For auditors, regulators, customers
   • DOCX — For internal editing
   • JSON — For GRC system integration
3. Save in compliance repository

Metadata stored:

• Generation date
• Last updated
• Author/approver (if signed)
• Version number
• Regulatory compliance references

Model Card Sections Explained

1. Model Details

Field	Content
Model Name	Official name and version
Developers	Organization and team
Date	Creation and last update dates
Model Type	LLM, classifier, recommender, etc.
Framework/Library	TensorFlow, PyTorch, scikit-learn, etc.
Model Size	Parameters, storage size, inference time
License	Open source or proprietary

2. Intended Use

Field	Content
Primary Use Case	What is the system designed to do?
Primary Users	Who uses it? (employees, customers, public)
Out-of-Scope Uses	What is it NOT meant to do?
Geographic Scope	Where is it deployed?
Decision Scope	Autonomous, assisted, or informational?

3. Training Data

Field	Content
Data Source	Where did training data come from?
Data Volume	Number of samples
Collection Period	Date range of data
Data Characteristics	Demographics, distributions, biases
Data Quality	Completeness, accuracy, issues known
Data Preprocessing	Cleaning, normalization, feature engineering
Known Limitations	Data gaps, temporal relevance, representativeness

Example:

Training Data: 100K customer service conversations (2021-2023)
Source: Company chat logs, anonymized
Demographics: 45% US, 35% EU, 20% other
Known Limitation: Underrepresents non-English languages;
may not generalize to customer populations <18 or >65

4. Model Performance

Metric	Meaning	Typical Threshold
Accuracy	% correct predictions	85%+ for most uses
Precision	% predicted positive that were correct	90%+ for high-stakes
Recall	% actual positives correctly identified	90%+ for safety-critical
F1 Score	Harmonic mean of precision and recall	0.85+
ROC-AUC	Area under ROC curve (discrimination)	0.85+
Latency	Response time	<200ms for real-time
Throughput	Predictions per second	Depends on use case

Format example:

Test Set Performance (holdout test set, 10K samples)
- Accuracy: 92.3%
- Precision: 89.1%
- Recall: 94.5%
- F1-Score: 0.918
- ROC-AUC: 0.945
- Latency: 145ms p95
- Throughput: 500 req/sec

5. Fairness & Bias

Assessment	What to Report
Disparate Impact	Do error rates differ by demographic group?
Demographic Parity	Do prediction rates match across groups?
Equalized Odds	Do false positive/negative rates match?
Group Performance	Accuracy per demographic group
Known Biases	Identified disparities and causes
Mitigation Strategies	How are biases being addressed?

Example:

Fairness Testing (test set, 5K samples across demographics)
- Gender: Female 91.2% accuracy, Male 92.8% accuracy (1.6% gap)
- Age: <30 91.5%, 30-50 92.1%, >50 90.8% (1.3% gap)
- Race: [testing framework dependent]

Disparate Impact (80/20 rule): None identified
Known Limitation: Sparse data for age >60, reduced reliability
Mitigation: Separate model validation for elderly users;
human review of high-stakes decisions

6. Known Limitations & Failure Modes

Category	Examples
Scope Limitations	Model trained on US data only; doesn’t work well internationally
Population Limitations	Model trained on adult data; not validated for minors
Data Drift	Model trained in 2022; may degrade as user behavior changes
Adversarial Robustness	Model can be fooled by adversarial examples
Edge Cases	Fails on rare inputs (unusual misspellings, edge cases)
Temporal Drift	Performance degrades over time

7. Recommendations

Type	Example
Deployment	”Use with human review for first 2 weeks; monitor false positive rate”
Maintenance	”Retrain monthly with new data; monitor accuracy drift >2%“
Monitoring	”Alert if accuracy drops below 90%; anomaly rate >5%“
Access Control	”Restrict to authorized staff; log all predictions”
User Communication	”Disclose to users: ‘This is an AI recommendation, not a guarantee‘“
Restriction	”Do NOT use for medical diagnosis; do NOT use for autonomous decisions”

8. Ethical Considerations (Optional)

Consideration	Content
Potential Harms	How could this system cause harm if it fails?
Bias & Fairness	Known biases; who is disadvantaged?
Privacy	What personal data is used? Can individuals be re-identified?
Transparency	Can users understand why they got a prediction?
Accountability	Who is responsible if system fails?

Example Model Card: Loan Approval AI

System Name: Fast Loan v2.1 Type: High-Risk (autonomous financial decision)

Intended Use: Assist bank loan officers in evaluating credit applications. Bank retains final decision authority.

Training Data:

• 500K historic loan applications (2015-2020)
• Features: Age, income, credit history, loan amount, employment
• Bias: Overrepresents urban borrowers (70%), underrepresents rural (30%)
• Known Limitation: Does not include alternative credit data; may disadvantage underserved populations

Performance:

• Accuracy: 88.2%
• Precision (approve): 91.5%
• Recall (default): 85.3%
• ROC-AUC: 0.920

Fairness:

• Age disparity (younger approved 4% more often; within 80/20 rule)
• Gender: No significant disparity detected
• Race: Insufficient data, not assessed
• Mitigation: All borderline cases (approval probability 40-60%) reviewed by human officer

Known Limitations:

• Not validated on borrowers <25 or >70
• Does not account for gig economy income (not in training data)
• May not generalize to non-English speaking applicants
• Performance degrades if economic conditions shift dramatically

Recommendations:

• Always use human review; this is not autonomous
• Monitor approval rate by demographic quarterly
• Retrain annually with new data
• Alert if accuracy drops <86% or approval rate changes >5%

Regulatory Notes:

• EU AI Act: High-Risk (autonomous financial decision)
• Requires: DPIA, bias testing, audit trail, human oversight — All documented and implemented
• Incident reporting: Article 73 playbook deployed

Using Model Cards for Compliance

Model cards are evidence for:

Framework	Usage
EU AI Act	Annex IV high-risk AI system documentation
GDPR	DPIA supporting document (data & bias assessment)
ISO 42001	Control 4.6 (data & model quality) evidence
SOC 2	Processing Integrity (PI) evidence
NIST AI RMF	Measure function documentation

Export model card and include in audit-ready reports.

Next Steps

• Generate your first model card: Go to Registry > [System Name] > Model Card > Generate
• View examples: See “Example Model Card” above
• Export for audit: Click Export > PDF
• Link to DPIA: DPIA & Algorithmic Assessment