alignment-review

Alignment Review

This is the last skill you run before declaring any AI work complete. It is a structured self-audit that catches responsible AI failures before they reach production or users.

You cannot mark this task done until every section is answered.

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Section 1: Goal Alignment

The system must do what it was asked to do — and only that.

What was the original goal or requirement?
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[Write it here from memory, without looking at the spec]

Now check: does your implementation match?
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[ ] The system solves the stated problem
[ ] The system does not do anything beyond the stated problem
[ ] There are no undocumented side effects or data accesses
[ ] The system's scope has not expanded during implementation without approval

If any box is unchecked: stop. Discuss with the requester before proceeding.

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Section 2: Fairness and Bias

[ ] bias-assessment skill was completed and findings documented
[ ] fairness-testing skill was completed and all tests are passing in CI
[ ] Disaggregated performance metrics are recorded
[ ] No protected group has performance below the defined minimum threshold
[ ] The bias assessment findings are committed to docs/

If any test is failing: you may not ship. Fix the model first.

Run the verification:

pytest -m fairness -v

All tests must pass. Green only.

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Section 3: Explainability

[ ] explainability-by-default skill was completed
[ ] Every prediction or decision the system makes can be explained
[ ] The explanation has been reviewed by a non-technical person and understood
[ ] The explanation is accessible to the people affected by the decision
[ ] Limitations of the explanation are documented

Spot-check:

# Run this on 5 random test cases
for i in range(5):
    sample = test_df.sample(1)
    explanation = model.explain(sample)
    assert explanation['plain_english'] != ""
    print(f"Case {i}: {explanation['plain_english']}")