Class imbalance occurs when one or more classes are underrepresented in the training data. The GREATEST concern is model bias (C): the model may learn to favor the majority class, leading to poor performance and unfair treatment for minority classes. In high-stakes applications (e.g., fraud detection, credit scoring, medical diagnosis), this can translate into systematic discrimination or incorrect decisions. AAIA highlights class imbalance as a common source of bias and stresses mitigation techniques (resampling, reweighting, threshold adjustments).