A significantly higher rejection rate is a clear indicator of potential algorithmic discrimination .
Management's PRIMARY response should be to conduct a comprehensive bias analysis (C), including fairness metrics, root-cause analysis, model explainability assessments, and data quality reviews. AAIA prioritizes fairness auditing and bias remediation as central to AI governance.
Option A is unacceptable because fairness issues fall outside most risk tolerances. Option B is a procedural check, not the solution. Option D (synthesizing data) might help but only after the root cause is identified-it is not the primary first step.
References:
ISACA, AAIA Exam Content Outline - Domain 1: Bias, Fairness, and Transparency Evaluations.