Periodic testing and monitoring for bias is a sustainable, proactive strategy aligned with best practices outlined in the AAIA™ Study Guide. This approach ensures that the AI system remains compliant over time, even as data and hiring conditions change.

“Ongoing fairness assessments help detect emerging biases and ensure that the AI model maintains equitable decision-making standards. Periodic testing also allows organizations to take corrective action before regulatory or reputational damage occurs.”

Suspending the system (B) or relying solely on external datasets (C) are temporary or limited in scope. Manual reviews (D) are effective but do not solve the root issue. Therefore, A provides a comprehensive, audit-aligned solution.