Delivering responsible AI with model cards

Introduction

The customer-facing teams need visibility into assumptions, limitations, and risks. The product team needs to know how a solution performs reliably. While the business team needs to understand capabilities and value.

How do we deliver useful user-facing documentation and make it easy to answer technical questions from customers?

We needed a consistent, accessible way to communicate what each solution is for, how it works, its strengths, and its limitations. That’s why we introduced model cards into our AI product development process.

Model cards offer a structured way to capture essential information about an AI solution's purpose, data, performance, and limitations. As a company committed to building AI systems that are explainable, human-centred, and trustworthy, model cards have become a key part of how we align innovation with governance.

In this post, we’ll explore what model cards are, why they matter for responsible AI, and how they can support cybersecurity and risk management goals in real-world deployments.

What Is a Model Card and Why Does It Matter?

A model card is a standardized document that provides essential information about a machine learning model. What it does, how it works, what the solution was designed for, and any boundaries around the solution.

First proposed by Mitchell et al. in 2018 in a paper "Model Cards for Model Reporting", model cards are now considered a foundational tool for responsible AI and model governance.

At Panaseer, we think of model cards as a nutritional label for AI. Just like a food label helps consumers understand ingredients and health impacts, a model card helps users, whether data scientists, product managers, or business stakeholders, grasp an AI solution’s purpose, strengths, technical design choices, and limitations.

It answers critical questions such as: Who is the solution for? What data was it trained on? What third-party components were used? Where might it introduce bias or error?

Model cards help clarify complex AI systems by making their behaviour explainable and their risks visible. In adopting and building AI solutions tailored to manage cyber risk, this transparency is not just helpful; it is essential. It supports informed decision-making, generation of user documentation, and alignment with governance frameworks like the NIST AI RMF, EU AI Act, and ISO 42001.

Model cards turn opaque AI into accountable AI. Here’s how they support responsible AI development and deployment at Panaseer:

• Increase transparency by clearly documenting what an AI solution does, how it was built, and where it performs best or may struggle.
• Support explainability by making solution behavior and design decisions understandable to both technical and business stakeholders.
• Enable accountability with a clear record of development context, performance metrics, and known limitations.
• Promote fairness by identifying potential bias and tracking solution performance across different populations or environments.
• Guide responsible use by flagging appropriate applications and helping teams avoid unintended misuse.
• Enhance trust by giving teams confidence in how solutions are built and deployed and the resources to support our users in getting value from the functionality.

Components of the Panaseer AI model card

A model card should ideally contain sections covering:

• Basic information and solution overview
• Intended use cases and limitations
• Responsible AI design and governance considerations
• Operational guidance and performance optimization

Our AI model card begins with the essentials such as the solution’s name, version, release date, and owning team. While straightforward, this information is vital for clear accountability and tracking the solution’s evolution.

Equally important is assessing the solution’s risk level, aligned with standards like the EU AI Act, which guides the level of oversight and documentation required to ensure safety and respect for fundamental rights.

We then clearly outline the solution’s intended use cases and limitations to help teams understand where it works best and where it might fall short so they can avoid unintended risks or misuse.

Transparency is a cornerstone of the card, including detailed information about the training data’s origins and processing, as well as any third-party components. This openness supports early bias detection, privacy protection, and consistency.

Additionally, the card emphasizes explainability by describing how the solution makes decisions in terms understandable to both technical and business users, fostering trust across stakeholders.

Finally, the model card ties into Panaseer’s AI principles, ensuring compliance with legal requirements, delivering tangible value, and actively managing risk throughout the solution’s lifecycle. By embedding these ethical commitments into the card, it becomes more than just documentation. It acts as a practical guide for building AI that is responsible, explainable, and trustworthy.

The essential role of AI model cards in Enterprise AI adoption

As enterprise AI adoption accelerates, so does the need to build and adopt responsibly. With nearly half of governments worldwide expected to enforce formal AI regulations by 2026, according to Gartner, organizations are being pushed to meet rising standards around transparency, fairness, and risk management. The EU’s AI Act outlines baseline standards for transparency and risk management, and the U.S. Federal Trade Commission is pushing for explainability and fairness with other regulations on the way.

Model cards are emerging as a key tool in this shift. They offer structured documentation that outlines a solution’s purpose, how it works, where it performs well, and what its limitations are. This not only supports regulatory compliance, but it also helps teams collaborate more effectively, spot bias early, and improve audit readiness. In fact, 68% of AI practitioners say detailed documentation is essential to ensuring fairness and minimizing risk.

Model cards also align closely with technical frameworks like the NIST AI Risk Management Framework, helping organizations map AI solution context, measure performance, manage risks, and improve governance. At Panaseer, they’re a foundational part of how we build AI that delivers value, earns trust, and meets the demands of modern enterprise risk management.