5 Things to Know About AI Model Cards

Summary

This IAPP educational article breaks down the essential elements of AI model cards—the transparency documents that companies use to provide crucial information about their AI systems. Rather than offering abstract theory, the piece takes a practical approach by examining how industry leaders like Google and IBM actually implement model documentation in practice. If you've been wondering what model cards should contain, how they differ from other documentation approaches, or why they're becoming a cornerstone of AI governance, this resource provides clear, actionable insights from real-world implementations.

What Makes Model Cards Different from Other Documentation

Model cards aren't just technical specifications or user manuals—they're purpose-built transparency tools designed to bridge the gap between AI developers and stakeholders who need to understand system behavior without diving into code. The article explains how model cards go beyond traditional software documentation by focusing on:

• Performance across different demographic groups rather than just overall accuracy metrics
• Known limitations and failure modes that users should expect
• Intended use cases and explicit non-uses to prevent misapplication
• Training data characteristics that influence model behavior

This approach differs significantly from system cards (which focus on deployment context) or technical documentation (which emphasizes implementation details).

Real-World Implementation Insights

The resource stands out by showcasing how major tech companies actually approach model documentation rather than just describing theoretical best practices. You'll learn about:

• Google's Model Card Approach: How they structure information to make complex AI systems accessible to non-technical stakeholders while maintaining technical rigor.
• IBM's Documentation Strategy: Their methods for balancing transparency with competitive considerations, showing how companies can be open about model characteristics without revealing proprietary details.

These concrete examples help readers understand not just what model cards should contain, but how to make them practical and useful in real organizational contexts.

The Five Core Elements Decoded

The article breaks down the essential components that make model cards effective governance tools rather than mere compliance exercises. Each element is explained with context about why it matters for different stakeholder groups—from compliance teams needing audit trails to product managers assessing deployment risks.

The resource helps readers understand how these elements work together to create a complete picture of AI system behavior and limitations, making it easier to implement similar documentation practices in their own organizations.

Who This Resource Is For

• AI governance professionals looking for practical examples of transparency documentation in action, particularly those tasked with creating or evaluating model cards for their organizations.
• Compliance and legal teams who need to understand what constitutes meaningful AI system documentation beyond basic technical specs.
• Product managers and business leaders responsible for AI deployment decisions who need to understand what information should be available about AI systems before deployment.
• Technical teams new to AI governance who want to see how documentation practices translate from academic concepts to industry implementation.

Getting Started with Model Cards

The article provides enough context to help readers begin implementing model card practices immediately. While it doesn't offer templates, the real-world examples and clear explanations of core elements provide a solid foundation for developing organization-specific approaches to AI transparency documentation.

Consider this resource your primer before diving into more detailed implementation guides or regulatory requirements that mandate specific documentation approaches.