Human review degrades into a rubber stamp faster than governance recognizes — and the data is now formal.
MIT Sloan Management Review’s expert panel names rubber-stamp oversight as a governance failure mode. In one cited study of 450 clinicians, accuracy fell from 73% to 61.7% when they were given biased AI assistance — because they deferred to the machine.
“There’s a human in the loop” is the most common answer to how an AI workflow is governed. It is also the one most likely to be quietly false. A June 2025 MIT Sloan Management Review panel — with practitioners from DBS Bank, Stanford, and others — named the failure directly: without insight into how a model reaches its conclusions, “oversight becomes superficial, reducing human involvement to a rubber stamp rather than acting as a critical check.”
The effect is measurable. The panel cites a study of 450 clinicians in which diagnostic accuracy fell from 73% to 61.7% when participants were given deliberately biased AI assistance — not from lack of knowledge, but from deference to the machine. This is automation bias, and it gets worse precisely when the AI is usually right: when 95% of outputs are fine, reviewers learn to approve on autopilot and miss the 5% that matter.
The test that exposes it
You do not need an audit to find out whether your review is real. Pull the data you already have: the median time between “item arrives” and “approved,” and the rejection rate. If the median is a handful of seconds and the rejection rate is near zero, the checkpoint is recording presence, not judgment — and it is absorbing liability without adding assurance.