Question

A bank deployed a loan-approval ML model six months ago. Regulators now require the bank to demonstrate that the model's approval outcomes do not systematically disadvantage applicants based on protected attributes like gender, using live production data. Which approach best satisfies this requirement?

Accepted Answer

Use SageMaker Clarify to compute post-training bias metrics (such as difference in positive proportions in predicted labels) on the model's production predictions across protected groups

Answer

Enable Guardrails for Amazon Bedrock to block any loan responses that mention protected attributes

Answer

Increase the model's training data volume to improve overall accuracy

Answer

Publish a model card documenting the intended use and training process of the model