What is the purpose of Model Cards in AI?

• A. To provide a transparent overview of a model, including objectives, training data, capabilities, limitations, and performance
• B. To measure model depreciation over time
• C. To drive deployment automation
• D. To replace code documentation

Answer: (A)

Model Cards provide a transparent, standardized summary of a model, outlining its intended use, the data it was trained on, its capabilities and limitations, and how it performs across different scenarios. This clarity helps developers, managers, and other stakeholders understand what the model is capable of, where it might fail, and what risks or ethical considerations to watch for before and after deployment. By presenting objective information about objectives, training data, performance, and caveats, Model Cards support responsible decision-making and governance.

They aren’t primarily about measuring how a model’s value declines over time, nor about automating deployment processes, nor about replacing code documentation. Those aspects belong to model maintenance, operations, and software documentation, respectively, while Model Cards focus on communicating the model’s behavior and risks to enable safer, more informed use.