An AI model for supply chain disruption prediction is retrained quarterly using recent data. After the latest retraining, the model's predictions become significantly more conservative, flagging 40% fewer disruptions than the previous version. Before promoting the new version, what validation step is MOST important?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — b is correct because a 40% reduction in disruption flags could indicate either a genuine improvement (fewer false positives) or a dangerous regression (missing real disruptions). Back-testing on historical periods with known disruptions is the definitive test: if the new model fails to flag events that clearly occurred, it has regressed and should not be promoted.
Full Explanation
B is correct because a 40% reduction in disruption flags could indicate either a genuine improvement (fewer false positives) or a dangerous regression (missing real disruptions). Back-testing on historical periods with known disruptions is the definitive test: if the new model fails to flag events that clearly occurred, it has regressed and should not be promoted. Infrastructure verification (A) is an operational compliance check, not a model quality check. Stakeholder surveys (C) provide qualitative confidence but cannot replace empirical back-testing. Training data volume (D) is a necessary condition but not sufficient to explain or validate the behavior change.