AI Model Card
An AI model card is a standardized document that reports a model's intended use, training data characteristics, evaluation results, known limitations, and fairness or bias findings, published alongside the model so anyone integrating it — including QA — knows what it was actually tested for before treating it as a black box.
Model cards emerged because "the model works" is meaningless without knowing for what — a model evaluated only on English customer support transcripts may perform very differently on technical documentation or another language, and without that context a QA team has no way to know which of their test scenarios fall outside the model's validated range.
For testing purposes, a model card is a starting map of what not to assume is covered: intended use cases, known failure modes, and reported evaluation metrics tell a QA team where the vendor's or ML team's own testing already went deep, and — just as importantly — where it didn't, which is exactly where new test coverage is most needed.
The practical risk is treating a model card as sufficient testing rather than a starting point — a vendor's reported benchmark numbers reflect their evaluation data, not your production traffic, so model card claims still need to be independently verified against your own real-world test cases before they're trusted.
