Model Card
A documentation framework for machine learning models that describes their intended use, performance characteristics, limitations, and ethical considerations.
What It Includes
Model details (architecture, training data, parameters), intended use cases and out-of-scope uses, performance metrics across different demographics, known limitations, training and evaluation data descriptions, and ethical considerations.
Why It Matters
Model cards promote transparency and accountability. They help users understand whether a model is appropriate for their use case and what biases or limitations to expect. Major AI labs now publish model cards with their releases.
Best Practices
Include performance breakdowns by demographic group. Document failure modes. Be explicit about what the model should NOT be used for. Update the card as new information emerges.