ML model card captures thorough information such as datasets used for development, intended usage, failure cases and evaluation results and much more.
Continuing from my previous article on Responsible AI, where I summarised Google’s responsible AI progress report. I wanted to document further about the concept of “Model card” — its general structure, purpose and why it's needed in ML workflows.
A model card is a standardised documentation framework introduced to enhance transparency and accountability in ML systems. It provides critical metadata about an ML model. Typically, a well-structured model card could include sections such as:
Model cards act as an internal checkpoint during development and a communication tool for external stakeholders. They also support reproducibility, regulatory compliance, and responsible deployment practices, especially important as ML systems continue to be integrated into high-stakes domains. There are also multiple variations of model cards, such as those used for GPT-3 or models hosted on Hugging Face.
Essentially, I strongly believe the model cards are not one-size-fits-all. The depth and level of details often depend on who is using the model and how mature the team’s MLOps practices are. Nonetheless, model cards should be treated as living documents, something you revisit, refine and improve over time as part of building AI responsibly and transparently.