Safety, privacy, and governance · Lesson 7 of 15 · 5 min

Read a model card

A model card records intended uses, limits and evaluation context. It is an evidence source for review, not a guarantee of suitability for every deployment.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-safety-governance-007.json. Separate the recorded facts from your assumptions.
Hint & worked example
A model card records intended uses, limits and evaluation context. It is an evidence source for review, not a guarantee of suitability for every deployment.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-safety-governance-007.json
```
Common mistake to avoid: A model card authorizes every possible use of the model.
2
Isolate the deciding signal
Select the within_scope field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Do not approve the proposed use from an internal-summarization model card.
A successful JSON query isolates within_scope.
```
jq .within_scope /opt/ai-lab/cases/aiops-safety-governance-007.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: reject-scope-expansion, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-safety-governance-007-decision.txt.
Hint & worked example
Do not approve the proposed use from an internal-summarization model card.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'reject-scope-expansion' > /home/learner/lab/aiops-safety-governance-007-decision.txt
```
Common mistake to avoid: A model card authorizes every possible use of the model.

Understand the pattern

A model card records intended uses, limits and evaluation context. It is an evidence source for review, not a guarantee of suitability for every deployment.

Interpret the evidence

Do not approve the proposed use from an internal-summarization model card.

A tempting mistake

A model card authorizes every possible use of the model.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-safety-governance-007.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "intended_use": "internal summarization",
  "proposed_use": "autonomous clinical decisions",
  "within_scope": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .within_scope /opt/ai-lab/cases/aiops-safety-governance-007.json

false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'reject-scope-expansion' > /home/learner/lab/aiops-safety-governance-007-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “reject-scope-expansion” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read a model card” justify?A model card authorizes every possible use of the model.Make the change now and gather the missing evidence afterward.Do not approve the proposed use from an internal-summarization model card.

Apply it elsewhere

A colleague concludes: “A model card authorizes every possible use of the model.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Review approved use Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision