AIF-C01 Guidelines for Responsible AI Practice Question
A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that increasing the detection threshold improves model performance overall, when in fact it only reduces false positives at the cost of lowering recall, which can be detrimental in high-stakes applications like fraud detection.
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
✓
Tune the model to achieve an acceptable balance between recall and precision
Responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Send all flagged transactions to customers for confirmation
Why it's wrong here
Customer fatigue may occur.
- ✗
Focus only on precision to minimize false positives
Why it's wrong here
Ignoring recall can miss fraud.
- ✓
Tune the model to achieve an acceptable balance between recall and precision
Why this is correct
Balancing metrics is a responsible approach.
- ✗
Increase the detection threshold to reduce false positives
Why it's wrong here
Higher threshold reduces recall more.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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