What Is an AI Model Card? A Plain-Language Guide for Technical Writers
When your organisation deploys an AI model, who is responsible for explaining what it does, what it should not do, and what happens when it gets something wrong?
If the answer is "nobody," that is a problem. And it is a problem that is landing, increasingly, on the desks of technical writers.
AI models are being built and deployed at a pace that has outrun their documentation. Teams can tell you how a model performs on a benchmark. They are far less prepared to explain its training data, its known failure modes, or whether it has been tested against the populations it will actually affect. That gap is closing, slowly, under pressure from regulators, enterprise procurement teams, and the growing expectation of transparency around AI systems.
The document that closes that gap has a name: a model card.
If you have not encountered one yet, you will. This article explains what model cards are, why they matter, and what you need to know to start writing one.