AI Model Validation: Beyond the Easy Cases to Uncover True Risks

This article emphasizes that effective AI model validation must go beyond testing easy, in-distribution data. Internal auditors should ensure testing protocols actively seek out model failures in hard cases, across diverse subgroups to detect bias, and under adversarial conditions to assess robustness. This proactive, skeptical approach is crucial for identifying and mitigating significant AI risks before deployment, aligning closely with core auditing principles.