Model Cards and AI Documentation

What is a Model Card? A standardized document describing an ML model, including its capabilities, limitations, intended use, and potential risks.

Model Card Sections

Basic Information

# Model Card: [Model Name]

## Model Details
- Developer: [Organization]
- Model type: [Architecture, e.g., Transformer]
- Model size: [Parameters]
- Training data: [Description]
- Training procedure: [Brief methodology]
- Model date: [Released date]

Intended Use

## Intended Use
- Primary use cases: [Applications]
- Out-of-scope uses: [What NOT to use for]
- Users: [Target audience]

Performance

## Performance
| Benchmark | Score | Notes |
|-----------|-------|-------|
| MMLU | 85.3 | General knowledge |
| HumanEval | 72.1 | Code generation |
| MT-Bench | 8.9 | Conversation |

Limitations and Risks

## Limitations
- Factual errors: May hallucinate
- Bias: [Known biases]
- Safety: [Potential harms]
- Languages: [Supported/tested languages]

## Ethical Considerations
- [Privacy concerns]
- [Potential for misuse]
- [Environmental impact]

System Cards (for AI Systems) Extends model cards for deployed systems:

• User interface considerations
• Deployment context
• Monitoring and feedback mechanisms
• Incident response procedures

Data Cards Document training datasets:

## Data Card

### Dataset Description
- Source: [Where data came from]
- Size: [Number of samples]
- Collection: [How it was gathered]

### Composition
- Demographics: [Representation]
- Languages: [Coverage]
- Time period: [When collected]

### Preprocessing
- Filtering: [What was removed]
- Anonymization: [Privacy measures]

Tools

Best Practices

• Update cards as models evolve
• Be specific about limitations
• Include quantitative metrics
• Document known failure cases
• Provide example use cases