A data scientist wants to assess pretraining bias in loan amount distributions relative to categorical variables (such as loan type and region). Which pretraining bias metrics should be used to compare distributions? (Choose three.)

Jensen-Shannon divergence, Kullback-Leibler divergence, and Total variation distance are all appropriate metrics for comparing probability distributions, which is essential for assessing pretraining bias in loan amount distributions relative to categorical variables. These metrics quantify the difference or similarity between two probability distributions. Class imbalance is a measure of the distribution of classes in a dataset, not a direct comparison of two distributions. Conditional demographic disparity is a post-training bias metric that evaluates model performance across different demographic groups, not a pretraining distribution comparison. Difference in label proportions measures the difference in the proportion of a specific label between groups, which is a specific type of distribution comparison but not as general or comprehensive as the divergence measures.