What is a model card report? Your guide to responsible AI

With new regulations like the EU AI Act on the horizon, the era of deploying AI without rigorous documentation is over. Regulators, customers, and other stakeholders are demanding proof that your models are fair, safe, and transparent. Simply stating that your AI works is no longer enough; you have to show your work. This is where model cards become essential.

For any enterprise leader, understanding a model card report is now a critical part of your compliance strategy. It serves as the official record of a model’s purpose, usage, testing, and limitations, providing the evidence auditors need to satisfy and build trust. It’s your primary tool for demonstrating due diligence in an increasingly regulated environment.

What is a model card?

Think of a model card as a cross between a nutrition label and an instruction manual for an AI model. At its core, a model card is a structured overview of how an AI model was designed and evaluated. It’s a key artifact in any responsible AI framework, aiming for transparency in development. By clearly outlining a model’s components and performance metrics, model cards help everyone involved understand its specific strengths and weaknesses. This is especially critical when models are used for high-stakes decisions, like predicting health risks or financial outcomes. They provide the necessary details about how well a model works under various conditions, which is fundamental for risk management and ethical oversight.

Model cards can help satisfy compliance requirements under laws such as the EU AI Act, Colorado SB205, and NYC Local Law 144. It’s not just another piece of documentation to file away; it’s an active tool that brings clarity and structure to your AI systems. By making model cards a standard part of your workflow, you create a powerful framework for managing risk and building trust with everyone who interacts with your AI.

While the exact format can vary, a useful model card typically includes several key sections that provide a complete picture of the model:

• The model’s name and version
• Its intended use cases
• Details about its architecture
• How to monitor the model
• The data used for training
• Key performance metrics
• Known limitations or ethical considerations, such as potential biases or scenarios where the model is likely to underperform

How to create an effective model card

Creating a model card isn’t just another box to check in your development process — it’s an exercise in clarity and responsibility. A well-constructed model card acts as a central source of truth, providing a clear, honest summary of your AI model for anyone who needs to understand it, from fellow developers to customers to the C-suite. The goal is to build a document that is both comprehensive in its technical details and accessible in its language. The process starts with determining what information to gather, then committing to presenting it with absolute clarity.

• Write for clarity and accessibility: The primary goal is transparency, so avoid overly technical jargon wherever possible. When you must use specific terminology, explain it in simple terms. Your audience includes compliance officers, legal teams, and business leaders who need to make informed decisions but may not have a background in machine learning.
• Structure the information logically: Use clear headings and bullet points to make the document scannable. Incorporate visualizations, such as charts and graphs, to illustrate performance metrics and make complex data easier to digest.
• Create a framework for evaluation: Model cards should establish a standardized framework for evaluating an AI model’s performance and limitations. These documents provide clear, concise details on how a model performs under various conditions (both expected conditions and edge-case scenarios), creating a baseline for ongoing monitoring and validation.

How to overcome implementation roadblocks

Putting model cards into practice presents a few common roadblocks, from inconsistent documentation to the sheer effort of manual creation. But these are far from insurmountable. With a clear strategy, you can build a process that makes creating and maintaining model cards a seamless part of your AI development lifecycle.

Model cards shouldn’t be an afterthought created right before deployment. To be truly effective, they must be living documents that evolve with the model. Integrate model card creation and updates directly into your development workflow, alongside code reviews and security scans. Update model cards whenever there are significant changes to the model, how it’s used, or who it’s used on.

This process should include documenting how the model performs on different data subsets and explicitly stating its intended uses and limitations. This practice helps you identify potential performance gaps or areas of concern early on. By making model cards an integral part of your machine learning development process, you transform transparency from a final-step chore into a continuous, value-adding habit.

Manually creating and updating model cards for every model is not scalable, especially in a large enterprise. It’s time-consuming and leaves room for human error. This is where automation becomes essential. The right tools can extract information directly from your development and monitoring systems to automatically populate sections of the model card.

About the authors

Guru Sethupathy is the VP of AI Governance at AuditBoard. Previously, he was the founder and CEO of FairNow (now part of AuditBoard), a governance platform that simplifies AI governance through automation and intelligent and precise compliance guidance, helping customers manage risks and build trust and adoption in their AI investments. Prior to founding FairNow, Guru served as an SVP at Capital One, where he led teams in building AI technologies and solutions while managing risk and governance.