Human-in-the-Loop AI: Proven Design Guide to Avoid Risk
A language model does not fail loudly; it fails fluently, producing a confident and entirely wrong answer that looks exactly like the correct ones before it. This guide treats human-in-the-loop design as an engineering problem rather than a governance slogan: the difference between in-the-loop, on-the-loop and in-command, the five review patterns and when each is appropriate, where in a workflow the checkpoint actually belongs, how to build a review interface that produces good decisions instead of fast ones, how to size the review workload before committing headcount, how to calibrate confidence thresholds against real outcomes, the five metrics that reveal rubber-stamping, and what an audit trail must record to satisfy the EU AI Act, UK GDPR and ISO/IEC 42001.
