G7 - Your Human Oversight Might Be Ceremonial. Here's the Test.
The dominant failure of human oversight isn't absence — it's automation bias. The human is present, trained, even logged. And approving everything, at a pace no genuine review could sustain.
Where this gets hard
- Oversight is designed on paper as review; in practice it degrades into acknowledgment.
- Reviewers can't catch failures they were never told to expect — capability statements without limitation statements are half a briefing.
- Overseers often lack real authority: declining the AI's recommendation requires courage, escalation, or both.
- Nothing measures engagement, so rubber-stamping stays invisible until an approved harm surfaces.
- Near-zero disagreement rates get celebrated as success rather than investigated as a symptom.
Where to start
- Log oversight as evidence: approvals, overrides, rejections and reasons, per event. Untracked oversight is unprovable oversight.
- Watch disagreement rates — and investigate near-zero as seriously as you'd investigate an error spike.
- Run seeded-error tests: plant plausible-but-wrong outputs and measure the catch rate — transparently, as a safety practice, never as covert performance management.
- Publish limitations alongside capabilities, in plain language, so reviewers know what wrong looks like.
- Give overseers tested authority to intervene, override and halt — and prove the halt path works before you need it.
The companion consulting document on our website includes an oversight-effectiveness test protocol and a redesign guide for ceremonial review.
Part of RMAT's 12-part series on AI governance — Governing AI with Evidence. The companion consulting document — detailed checklists, a risk table, a maturity self-assessment and a 90-day action roadmap — is available on our website. #CEO #CIO #CTO #AI #Risk #Governance
What is your reviewers' disagreement rate with your AI — and if you don't know, what does the oversight actually consist of?