Question

A bank deployed a loan-approval ML model six months ago. The team validated fairness across demographic groups before launch. Recently, customer complaints suggest approval rates have shifted unfavorably for certain groups, even though the model code has not changed. Which responsible-AI practice most directly addresses this situation?

Accepted Answer

Continuously monitor the deployed model for data drift and fairness degradation over time

Answer

Publish a model card documenting the model's intended use and training data

Answer

Increase the model's temperature setting to introduce more variability in outputs

Answer

Add a Bedrock guardrail to block requests containing personally identifiable information