What information is typically included in model cards?

Model cards are designed to provide a comprehensive overview of machine learning models, specifically highlighting their performance, intended use cases, and potential ethical implications. This information is crucial for understanding how a model behaves in practice and the contexts in which it may be effectively applied.

By including performance details, model cards show metrics such as accuracy, precision, and recall across different datasets. This helps users assess the model's reliability and suitability for specific tasks. Additionally, ethical considerations are critically important as they address potential biases, fairness issues, and the implications of deploying the model in real-world scenarios. Discussing these aspects encourages transparency and accountability in machine learning practices.

In contrast, while details about memory capacity, hardware specifications, and user interaction examples might be useful in other contexts, they do not capture the essential elements that model cards aim to communicate regarding model performance and ethical considerations.