An AI risk manager is evaluating a new computer vision model used to detect manufacturing defects. The model achieves 97% overall accuracy, but the risk manager is concerned about performance on rare defect types. Which metric BEST captures risk related to rare defect classes?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — c is correct because per-class recall directly measures the model's ability to correctly identify each defect type, including rare ones. High overall accuracy can mask poor performance on minority classes (accuracy paradox).
Full explanation below image
Full Explanation
C is correct because per-class recall directly measures the model's ability to correctly identify each defect type, including rare ones. High overall accuracy can mask poor performance on minority classes (accuracy paradox). AUC-ROC on the full set also aggregates performance and can obscure rare-class failure. F1 on only the common class ignores the rare defect risk entirely.