ISTQB Certified Tester AI Testing Practice Test

A model card is a concise, human-readable summary that states a model’s intended use, how it performs, its known limitations, and the risks it may pose in real-world use. This kind of documentation is central to AI testing and governance because it provides a clear, standardized way for testers, product teams, regulators, and other stakeholders to understand when and where the model should be used, what performance to expect, and what potential harms or failures to anticipate.

Think of it as a governance-oriented label for the model. It helps teams plan validation and monitoring, compare different models, and justify deployment decisions. A model card typically covers the model’s purpose and audience, the data it was trained on, evaluation metrics and results, performance across different groups or scenarios, known limitations and failure modes, potential risks and mitigations, the intended deployment environment, and how the model will be monitored and updated over time. It also notes who owns the model and where to find the latest version.

This is not about hardware specifications, coding style guides, or data schemas. Those documents serve different purposes; the model card focuses on use cases, performance, limitations, and risk to support responsible deployment and ongoing governance.