Question

Your team deployed a credit-approval model on Vertex AI. Regulators require ongoing evidence that the model does not produce discriminatory outcomes across protected groups such as gender and ethnicity. The model shows strong overall accuracy, but leadership is concerned about fairness in production. Which approach best addresses this responsible-AI risk during ongoing monitoring?

Accepted Answer

Continuously monitor approval rates and error rates disaggregated by protected group using fairness metrics such as demographic parity difference and equal opportunity difference, and alert when disparities exceed defined thresholds.

Answer

Rely on the model's overall accuracy and AUC in production, since a high-performing model is inherently fair across all subgroups.

Answer

Remove the protected attributes from the training features so the model cannot use them, which guarantees the outcomes are unbiased.

Answer

Enable feature-attribution drift monitoring on the input features and treat any drift alert as evidence of a fairness violation.