An AI Model Card is a structured documentation format that accompanies an AI model to communicate its key attributes, capabilities, limitations, and appropriate use cases. Introduced by Google researchers in 2019, model cards have become a best practice for AI transparency and accountability.
A comprehensive model card typically includes: model details (architecture, version, developer, release date), intended use (primary use cases and target users), out-of-scope uses (applications the model is not designed or tested for), training data (description of datasets used, including demographics and sources), performance metrics (accuracy, precision, recall across different conditions and populations), limitations (known weaknesses, failure modes, and edge cases), ethical considerations (potential biases, fairness evaluations, and societal impacts), and recommendations (best practices for deployment and monitoring).
Model cards serve enterprise governance in several ways: they support vendor evaluation when selecting AI tools, enable risk assessment for specific use cases, provide documentation for regulatory compliance, facilitate informed consent when AI affects end users, and create accountability by establishing clear expectations for model behavior.
Organizations should require model cards or equivalent documentation from all AI vendors, create internal model cards for custom-built AI systems, and reference model cards in AI risk assessments and governance reviews.
