Evaluation Risk Battlecards
Respond to misleading neural-network evaluation claims with a sharper metric, evidence, or control request.
Accuracy claim The model is 93 percent accurate, so it is ready. A team wants to move from validation to deployment. Your line 93 percent on which split, for which segments, and with what false-positive and false-negative costs? Do not argue against accuracy. Ask what it hides. It turns a headline metric into evaluation evidence. Confidence claim The model is very confident on these predictions. A stakeholder treats confidence as correctness. Confidence needs calibration evidence. Show whether high-confidence predictions are actually right on held-out and live data. Confidence is a model output, not proof. It asks for calibration instead of…
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