AIF-C01 Guidelines for Responsible AI Practice Question
Which THREE considerations are essential for ensuring responsible AI in a model that predicts employee performance? (Choose 3)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that transparency means public disclosure of all model outputs, whereas in responsible AI, transparency refers to explainability and auditability of the model's logic, not exposing sensitive predictions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Incorporate human review before final decisions
Responsible AI frameworks, such as those outlined by AWS, mandate human-in-the-loop (HITL) oversight for high-stakes decisions like employee performance predictions. This ensures that automated outputs are reviewed for context, fairness, and potential errors before affecting employment outcomes, aligning with the AIF-C01 domain's emphasis on human accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Minimize the number of features to reduce cost
Why it's wrong here
Cost minimization is not a responsible AI principle.
- ✗
Publish the model's predictions publicly for transparency
Why it's wrong here
Public disclosure may violate privacy.
- ✓
Incorporate human review before final decisions
Why this is correct
Human-in-the-loop reduces automation bias.
- ✓
Ensure employee data privacy and consent
Why this is correct
Privacy is a key ethical consideration.
- ✓
Test for bias across demographic groups
Why this is correct
Bias testing is essential for fairness.
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