What is Model Card?
A Model Card is a standardised documentation framework that describes an AI model's intended use, performance characteristics, training data, limitations, and ethical considerations, providing stakeholders with the information needed to understand and responsibly deploy the model.
What is a Model Card?
A Model Card is a structured document that accompanies an AI model and provides essential information about how the model was built, what it is designed to do, how well it performs, and what its limitations are. The concept was introduced by researchers at Google in 2019 as a way to standardise AI model documentation and promote transparency.
Think of a model card as a nutrition label for AI. Just as a food label tells you what ingredients are in a product, a model card tells you what went into building an AI model and what you should know before using it. It provides a concise, accessible summary that enables both technical and non-technical stakeholders to make informed decisions about whether and how to use a particular model.
Why Model Cards Matter
Bridging the Information Gap
In many organisations, the people who build AI models are not the same people who decide where and how those models are deployed. Business leaders, product managers, compliance officers, and operational teams all need to understand what a model can and cannot do. Model cards bridge this information gap by presenting model information in a standardised, readable format.
Supporting Responsible Deployment
Without adequate documentation, models may be deployed in contexts they were not designed for. A model trained on data from one market might be applied in another where its performance characteristics differ significantly. A model designed for low-stakes recommendations might be repurposed for high-stakes decisions. Model cards help prevent these misapplications by clearly stating intended uses and known limitations.
Enabling Compliance
Regulatory frameworks across Southeast Asia and globally are increasingly requiring documentation of AI systems. Singapore's AI Verify testing framework expects organisations to document their models' characteristics. The EU AI Act mandates detailed technical documentation for high-risk systems. Model cards provide a practical format for meeting these documentation requirements.
What a Model Card Contains
Model Details
This section describes the basics: the model's name, version, type (classification, regression, generation, etc.), the team or organisation that built it, and the date it was created. It also specifies the framework and libraries used in development.
Intended Use
The model card clearly states what the model is designed to do and the contexts in which it should be used. Equally important, it specifies out-of-scope uses, situations where the model should not be applied. This section helps prevent misuse and sets clear expectations.
Training Data
This section describes the data used to train the model, including its source, size, composition, and any preprocessing applied. It should note any known biases in the data, demographic breakdowns if applicable, and the time period the data covers. For organisations in Southeast Asia, this section should address whether the training data represents the diverse populations the model will serve.
Performance Metrics
Model cards report the model's performance using relevant metrics such as accuracy, precision, recall, F1 score, or AUC, depending on the task. Critically, performance should be reported not just in aggregate but broken down by relevant subgroups. A model might achieve 95 percent accuracy overall but only 80 percent accuracy for a specific demographic group. This disaggregated reporting is essential for fairness evaluation.
Limitations and Risks
Every model has limitations. This section documents known weaknesses, failure modes, and scenarios where the model's performance degrades. It should describe the types of inputs that confuse the model, edge cases it handles poorly, and any systematic biases that testing has revealed.
Ethical Considerations
This section addresses broader ethical concerns related to the model. Does it process sensitive personal data? Could its outputs be used to discriminate? What privacy protections are in place? What steps were taken to address fairness concerns?
Recommendations
The model card concludes with practical recommendations for deployment, including suggested monitoring approaches, performance thresholds for acceptable use, and guidance for ongoing evaluation.
Creating Effective Model Cards
Start Early
Begin drafting the model card during the development process, not after the model is complete. As decisions are made about data, architecture, and training procedures, document them in real time. This is easier and more accurate than trying to reconstruct the information later.
Write for Multiple Audiences
A good model card serves both technical and non-technical readers. Use clear language and avoid unnecessary jargon. Provide technical details for data scientists and engineers, but ensure that business leaders and compliance officers can also understand the key points.
Be Honest About Limitations
The value of a model card comes from its candour. Documenting limitations is not a sign of failure but rather a sign of responsible AI development. Stakeholders need honest information about what a model cannot do in order to deploy it safely.
Update Regularly
Model cards should be living documents. Update them when the model is retrained, when new limitations are discovered, when the deployment context changes, or when performance monitoring reveals new information.
Include Disaggregated Metrics
Reporting only aggregate performance masks potential fairness issues. Break down performance metrics by relevant demographic groups, geographic regions, and other meaningful categories. In Southeast Asia, consider reporting performance across different languages, ethnic groups, and market segments.
Model Cards in Southeast Asia
Model cards are becoming increasingly relevant for organisations in Southeast Asia as the region's AI governance expectations mature. Singapore's AI Verify framework aligns well with the model card concept, encouraging organisations to document and test their models against governance principles.
For organisations deploying AI across multiple ASEAN markets, model cards serve a practical purpose beyond compliance. They help teams in different countries understand whether a model developed for one market is appropriate for deployment in another. A model trained primarily on data from Singapore may not perform equally well in Indonesia or Vietnam, and a well-written model card makes these limitations visible.
As Southeast Asian regulators develop more specific documentation requirements, organisations that already use model cards will have a significant head start in demonstrating compliance.
Model Cards transform AI from a black box into a documented, governable asset. For CEOs, they provide the transparency that boards, regulators, and customers increasingly demand. When a regulator asks how your AI system works, or when a customer questions an AI-driven decision, a model card provides a documented, defensible answer.
For CTOs, model cards are a practical engineering discipline. They force development teams to think critically about their models' limitations and intended use before deployment, catching potential issues early. They also enable better organisational knowledge transfer, as teams change and models are handed off, documentation ensures that critical context is not lost.
In Southeast Asia, where organisations often deploy AI across multiple markets with different regulatory requirements, model cards create a consistent documentation standard that can be adapted to meet local requirements. The investment in creating model cards is modest compared to the cost of deploying undocumented models and discovering problems after they affect customers or trigger regulatory scrutiny.
- Begin creating model cards during the development process rather than after deployment, when critical decisions and context are fresh.
- Write model cards for both technical and non-technical audiences, ensuring business leaders and compliance teams can understand key information.
- Report performance metrics disaggregated by relevant subgroups to reveal fairness issues hidden by aggregate numbers.
- Document limitations honestly and comprehensively, as this information is essential for responsible deployment decisions.
- Update model cards whenever the model is retrained, deployed in a new context, or when monitoring reveals new information about performance.
- For models deployed across Southeast Asian markets, document whether training data represents the populations in each market and report performance by region.
- Treat model cards as living documents that evolve with the model, not as one-time compliance paperwork.
Frequently Asked Questions
What is the difference between a model card and technical documentation?
Technical documentation typically covers the full engineering details of a model, including architecture specifications, training procedures, hyperparameters, and infrastructure requirements. A model card is a more focused, accessible summary designed for a broader audience. It highlights the information most relevant to responsible deployment: intended use, performance characteristics, limitations, and ethical considerations. Think of technical documentation as the detailed engineering manual and the model card as the executive summary that enables informed decision-making.
Who should create model cards?
Model cards should be created collaboratively. Data scientists and ML engineers provide technical details about the model, its training data, and performance metrics. Product managers contribute information about intended use cases and deployment context. Ethics and compliance teams add guidance on ethical considerations and regulatory requirements. The most effective approach is to make model card creation a shared responsibility within the team that develops and deploys the model.
More Questions
As of early 2026, model cards are not explicitly mandated by law in most Southeast Asian countries. However, the documentation practices they represent align closely with regulatory expectations. Singapore's Model AI Governance Framework and AI Verify toolkit encourage the kind of documentation model cards provide. The EU AI Act, which affects companies serving European customers, requires detailed technical documentation for high-risk systems. Given the clear trend toward documentation requirements, adopting model cards now is a practical way to prepare for evolving regulations.
Need help implementing Model Card?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how model card fits into your AI roadmap.