Assuring AI Evaluators: Independent Testing for Enterprise Quality Gates

An automated evaluator can approve an answer that is persuasive but wrong. When its verdict controls release, errors in the evaluator become errors in the quality process. Agreement with another model does not establish that either judgment is correct. This paper proposes an assurance framework combining explicit acceptance criteria, independently adjudicated reference cases, controlled error insertion, blinded comparison, and continuing audits. It separates factual correctness from presentation and treats unresolved evidence as a reason for escalation. The framework produces a testable evaluator specification, a severity-sensitive scorecard, and a bounded release decision. A fictional refund example demonstrates how high overall accuracy can conceal poor detection of consequential defects. The architecture and testing matrix are design proposals; no experimental or deployment findings are reported. An evaluator should earn authority within a defined task, evidence set, and operating environment. Its reliability depends on credible reference judgments, targeted challenge tests, and renewed scrutiny when the surrounding system changes.

Artificial Intelligence