What is the purpose of a model card in AI?

The purpose of a model card is to provide a brief overview of model performance and use cases. This essential tool in the AI governance framework encapsulates key information about the model, such as its intended applications, its performance metrics across various demographics or scenarios, and any limitations or biases identified during the evaluation process. By summarizing this information in a structured format, model cards help stakeholders understand how to appropriately use the model and assess its reliability in different contexts.

Model cards also facilitate transparency and accountability, which are crucial in responsible AI practices. They serve as a communication tool to bridge the gap between technical developers and end-users, ensuring that users are well-informed about the model's capabilities and restrictions. This understanding aids in fostering trust in AI systems and encourages ethical AI deployment.

The other options focus on specific aspects of model development but do not encapsulate the comprehensive purpose of a model card. Documenting training data is critical but is just one component of the broader insights provided in a model card. Similarly, listing algorithms or providing detailed implementation instructions may be useful for technical documentation, but they do not address the overall need for a concise, informative overview that conveys key performance and usage insights as effectively as a model card does.