Fig. 7.

AttributionScanner applied to the validation of a bird category classification model trained on the Waterbirds dataset. The user finds the correct model behavior (bird patterns) corresponding to a well-performed Slice 34 and annotates it as “core: birds” B and C. By switching the Attribution Mosaic to the confusion matrix view D and investigating the underperformed slices with accuracy sorting A, the user identifies a problematic Slice 32 that has high false negatives D1, which turns out to use spurious feature of land backgrounds (BG) to predict landbirds.