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AIF-C01 Practice Question 99: Subgroup Performance Evaluation

AWS Certified AI Practitioner (AIF-C01) · Test 10 · question 9 of 10

Guidelines for Responsible AImedium

A lender's loan approval model reports 94% accuracy on its test set. Before launch, an internal audit asks the team to demonstrate that the model does not perform noticeably worse for particular groups of applicants. Which step best addresses the audit's request?

Select one answer
Show answer and explanation

Answer:

  • D. Compute the metrics for each applicant group separately and compare them

AWS describes bias as *"imbalances in data or disparities in the performance of a model across different groups."* An overall score can hide a large gap: a model can be very accurate for a majority group and much less accurate for a smaller one. Slicing the metrics (accuracy, precision, recall, false positive rates) by group and comparing them is how the team makes those disparities visible (option D).

- A is wrong because a larger test set gives a more stable average, but the average can still mask poor performance on a subgroup. - B is wrong because other features, such as postal code, can act as proxies for the removed attributes. Without the attribute, the team also loses the ability to check group-level results. - C is wrong because more training targets overall accuracy. It does not reveal or reduce differences between groups and may increase overfitting.

Reference: Responsible AI

Practice Test 10 ›

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