Model cards on the How to Make AI Models track. A model card says what the model is for, what data it saw, metrics, slices, and limits. It is part of making the model, not paperwork after a scandal.
This lesson assumes you already worked through Designing a loss.
The idea in practice
Draft the card before launch. Include the split and the intended use. Say a use that is out of scope.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Model cards',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A card written by marketing with no metric definitions. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
List five headings for your card.
Self-check
- Say Model cards in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.