Question

A machine learning engineer is preparing a dataset stored in Amazon S3 to train a binary classification model that predicts loan default. Before training, the team wants to measure whether the training data over-represents or under-represents specific demographic groups (such as age bands) relative to the positive label, so they can document and mitigate potential bias. The team wants a managed capability that produces bias metrics like class imbalance and difference in positive proportions in labels with minimal custom code. Which approach best meets this requirement?

Accepted Answer

Run a SageMaker Clarify pre-training bias analysis job that computes bias metrics such as Class Imbalance (CI) and Difference in Proportions of Labels (DPL) across the specified facets.

Answer

Enable Amazon SageMaker Model Monitor data quality baseline jobs to detect demographic imbalance in the training dataset.

Answer

Use AWS Glue DataBrew to apply one-hot encoding on the demographic columns, which automatically rebalances the classes before training.

Answer

Configure SageMaker Ground Truth to relabel the minority-group records so the positive and negative labels are evenly distributed.