When auditing an AI system's data pipeline, the auditor discovers that the final training dataset has a much higher proportion of positive class labels than the actual population. What risk does this create?

B is correct because overrepresenting positive cases creates miscalibration — the model's predicted probability outputs will be inflated relative to actual population prevalence, causing overestimation of positive outcomes in production. This is particularly problematic in risk scoring applications where calibration drives decision thresholds. A is incorrect — more positive examples typically improves positive class performance. C is incorrect — negative examples exist. D is a different concept related to input distribution.