Sort data risks by when to catch them

Sort each computer-vision risk by the phase where it should be caught first. Setup: define task and labels Training: build signal responsibly Evaluation: prove generalization Deployment: monitor real-world behavior Annotators disagree on whether shallow scratches count as defects. The positive class appears in only 3 percent of training images. Frames from the same video clip appear in both train and test. A camera is moved 40 cm after rollout and the model score drops. The team has not decided whether the output should be a box or a pass/fail label. Augmentation flips text labels upside down even though production labels…

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