AIF-C01 practice question: Transparency and explainability (2 of 2)
Guidelines for Responsible AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A lender must explain each rejection with the main factors that drove that decision, and those reasons must match what the model actually used.
A deep learning model is 0.5 percentage points more accurate than a simple, transparent scorecard. The lender processes 20,000 applications a month.
Which approach BEST meets these requirements?
Choose one answer.
AA. Keep the deep model and publish its overall accuracy figure in every rejection letter so applicants see its reliability.
BB. Keep the deep model and have underwriters write each rejection reason by hand after reviewing the full application file.
CC. Deploy the transparent scorecard, whose reasons come straight from its inputs, and accept the small accuracy loss.
DD. Keep the deep model and publish its full network architecture so applicants can see how the model is built internally.
Show the answer and explanation
Answer: C
A. Wrong: Overall accuracy describes the model across all applicants. It does not say why this applicant was refused, so it fails the explanation requirement.
B. Wrong: Hand-written reasons can be accurate, but they cannot scale to 20,000 applications a month. They may also drift from what the model actually used.
C. Right: Correct. A transparent scorecard shows each applicant the factors behind the score, and those reasons match what the model used. The small accuracy loss is a reasonable price for explainable decisions. Learn more: Amazon SageMaker Model Cards (opens in a new tab)
D. Wrong: A network architecture describes how the model is built, not what drove one decision. Applicants would still not learn their main reasons.
Explanation
Explainability needs reasons that trace back to the factors the model actually used. A transparent model provides this directly, whereas a deep model needs extra explanation methods that give approximate reasons. This question tests balancing a small accuracy gain against the requirement to explain every decision at volume.
Want more on Transparency and explainability? The 5 full-length practice sets have 15 more Transparency and explainability questions, 325 in all, none of them in the free exam. One payment of $14.99, no subscription. See the practice sets →