Model Card Template

Summary

Florida Atlantic University's Model Card Template provides a practical, standardized framework for documenting machine learning models in a way that actually gets used. Unlike academic model card proposals that often remain theoretical, this template focuses on real-world implementation, offering a structured approach to communicate model performance, limitations, and appropriate use cases to diverse stakeholders. It addresses the common problem of inconsistent or missing model documentation that plagues AI deployment across industries.

What makes this different

This template stands out by prioritizing usability over academic completeness. Rather than overwhelming users with exhaustive documentation requirements, it provides a balanced framework that captures essential information without becoming a bureaucratic burden. The template is designed to be filled out by the people who actually build models, not just governance teams, making it more likely to be adopted in practice.

The resource recognizes that different stakeholders need different information - executives care about business impact and risk, technical teams need implementation details, and end users need to understand limitations and appropriate use cases. The template structure accommodates these varying needs within a single, coherent document.

How to put this into practice

Start by identifying your most critical models - those with the highest business impact or regulatory scrutiny. Use the template to document one model completely, treating it as a pilot to understand what information is readily available versus what requires additional data collection or analysis.

The template works best when integrated into your existing model development workflow. Rather than treating model cards as a post-deployment afterthought, incorporate the documentation process into model validation and testing phases. This approach ensures the information is fresh and accurate while distributing the documentation workload across the development cycle.

Consider creating lightweight versions for internal models and more comprehensive documentation for customer-facing or high-risk applications. The template is flexible enough to support both approaches while maintaining consistency in structure and key information categories.

Who this resource is for

• ML Engineers and Data Scientists who need a practical framework for documenting their models without extensive governance training. The template provides clear guidance on what information to include and how to present technical details to non-technical stakeholders.
• AI Governance Teams implementing model documentation standards across their organization. This template offers a proven starting point that can be customized to meet specific regulatory or business requirements while ensuring consistency across different teams and projects.
• Product Managers and Business Stakeholders who need to understand model capabilities and limitations for decision-making. The structured format makes technical model information accessible to non-technical audiences.
• Compliance and Risk Management Professionals working in regulated industries who need standardized documentation to support audit and regulatory requirements.

Implementation roadmap

Phase 1: Template Customization (Weeks 1-2)

Phase 2: Pilot Implementation (Weeks 3-6)

Phase 3: Process Integration (Weeks 7-10)

Phase 4: Scaling and Governance (Ongoing)

Common challenges and solutions

Challenge: Developers resist additional documentation burden

• Challenge: Information is scattered across teams and systems
• Challenge: Keeping documentation current as models evolve