An IS auditor analyzed an AI model scorecard and identified that training data was imbalanced.

Data imbalance (where one class significantly outweighs another) leads to biased models that perform poorly on minority classes. The ISACA AAIA™ manual suggests that " class-weighted loss " is a proactive technical control that penalizes the model more for misclassifying minority samples, effectively " balancing " the learning process. Stratified splitting ensures that every training and validation set maintains the same ratio of classes as the original data, preventing the model from failing to " see " minority cases during the testing phase. Tuning thresholds (Options B and C) happens after the fact, whereas weighting addresses the root cause during training.