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 confirmation shifts fraud detection onto the account holder, adding friction without improving the model's recall on small transactions. It is tempting because human review suits high-value or ambiguous alerts, but here it addresses neither the missed-fraud cause nor responsible-AI bias mitigation.
- ✗
Focus only on precision to minimize false positives
Why it's wrong here
Optimising precision alone entrenches the existing imbalance, leaving small-transaction fraud undetected. Precision-focused tuning is correct where false positives burden analysts, yet this scenario's stated gap is recall, so the approach contradicts the responsible-AI requirement to reduce missed fraud.
- ✓
Tune the model to achieve an acceptable balance between recall and precision
Why this is correct
Tuning to balance recall and precision directly addresses the low recall on small transactions, reducing missed fraud while keeping false positives acceptable. This aligns with responsible AI by mitigating harm from undetected fraud rather than optimising one metric alone.
- ✗
Increase the detection threshold to reduce false positives
Why it's wrong here
Raising the threshold makes the model even more conservative, suppressing further small-transaction detections and worsening the low recall the scenario identifies. Threshold tuning is the right lever when false positives dominate, but here precision is already high and recall is the deficiency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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