Explainability testing is essential for determining how and why an AI model produces specific outputs. Transparency is a key AAIA requirement, especially in regulated or high-impact environments.
Explainability testing ensures:
Stakeholders understand model decisions
Auditors can verify fairness and accuracy
Regulators can review decision logic
The organization can justify outcomes to affected individuals
Models align with ethical and governance expectations