How to Read an AI Model Card (and What's Missing From Most)

A model card is a short document describing an AI model: what it's for, how it was built and where it falls short. Learning to read one helps you choose and use AI more safely.

• Intended use: what the model is designed to do.
• Out-of-scope uses: what it shouldn't be used for.
• Training data: sources, types and known gaps (at least in summary).
• Evaluation: tests, benchmarks and results, ideally by group or language.
• Limitations: known failure modes and risks.
• Safety measures: testing, red-teaming and guardrails.
• Licence and terms: what you may and may not do.
• Version and date: so you know what you're using.
• Contact and reporting routes for problems.

1. Is my use case in scope?
2. How was it tested, and on what kind of data?
3. Does it work for my language, region and users?
4. What are the known weaknesses? Hallucination, bias and security issues?
5. What data does it send or store?
6. Can I use it commercially?
7. How are updates communicated?

• Vague claims with no evidence.
• Only best-case benchmarks.
• No mention of limitations.
• No version or date.
• Unclear licence.
• Marketing language instead of detail.

• System cards describing a deployed product and its safety measures.
• Data sheets for datasets.
• Transparency reports on usage and enforcement.
• Terms of service and privacy policies, which are legally important.

You don't need to read everything. Check intended use, limitations, data handling and licence before relying on a tool.

A model card won't tell you everything, but it shows how seriously a provider treats transparency. Read it before you build on it.