Transcript
A required click is not a control
Saying that a human remains responsible does not prove that human review is effective. The reviewer must be qualified to recognize the important error, see the evidence needed to challenge the output, have enough time and independence, and be able to reject, correct, escalate, or stop the workflow.
Challenge realistic critical errors
Evaluate the combined AI and human process under realistic volume, time pressure, and document quality. Seed known critical and noncritical errors. Measure whether reviewers detect them, inspect sources, override incorrect output, and take the correct action. Overall agreement can hide a dangerous critical miss.
Watch for automation bias
Monitor confirmed misses, false findings, reviewer overrides, acceptance without source inspection, recurring issue types, and workload. A falling rejection rate may mean better output, or it may signal weakened challenge. Human review is enough only when its effectiveness is designed, tested, documented, and maintained.