Design Review That Can Catch Errors

The concept

Putting a person at the end of an automated process does not automatically create meaningful oversight. Reviewers need relevant evidence, enough time, and permission to reject the output. They also need a clear standard for escalation. A review button that is routinely clicked without inspection provides little protection against systematic mistakes.

Worked example

A fictional grants office uses AI to draft plain-language descriptions of completed projects. Reviewers see the original project report beside the draft and must verify amounts, dates, and claimed outcomes. One reviewer notices that 'planned to serve 500 residents' became 'served 500 residents.' The interface highlights those fields and allows correction before publication. Review effort is included in the time comparison with manual drafting, so apparent speed does not hide transferred labor.

Limits and judgment

People can become accustomed to accepting fluent output, especially under deadline pressure. Sample completed reviews, study disagreement patterns, and check whether the process catches known planted errors in a controlled exercise. Review should be proportional to consequences; low stakes formatting changes and consequential factual claims need different levels of attention.

RETRIEVE. REASON. REMEMBER.

Knowledge check.

Choose one answer for each question, then check your reasoning.