Testing counselor intervention rates and error detection | AIGP
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Testing counselor intervention rates and error detection: Before approval, which evidence best resolves the

AIGP Understanding the Foundations of AI Governance Medium

Testing intervention and error detection addresses how increased autonomy changes practical oversight.

The question

A school district uses an AI system to recommend course placements. Initially, counselors review every recommendation; a proposed update would automatically enroll students unless counselors intervene. Before approval, which evidence best resolves the new oversight risk?

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  1. Comparison of recommendation accuracy before and after retraining.
    Accuracy comparison may show performance changes, but it does not establish whether reduced human intervention remains adequate for enrollment decisions.
  2. A student-facing explanation of how recommendations are generated.
    Explanations can support understanding, but they do not demonstrate that counselors can oversee automatic enrollment reliably.
  3. Testing counselor intervention rates and error detection after automation. ✓
    These results show whether counselors can still identify and correct errors when the system acts with greater autonomy.
  4. A larger dataset covering additional academic subjects.
    Broader data may improve coverage, yet it does not answer whether the revised workflow preserves effective human oversight.
The trap
When automation expands, test the revised human-control pathway, not only model metrics.

How to remember it

Testing intervention and error detection addresses how increased autonomy changes practical oversight.

How many of these would you get right?

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