Practice Library

Match the flow of AI output to the real capacity of the people who must check it — throughput honesty as a safety control.

Attach an independent checking step to the least-supervised operator — in PAN runs, the single highest-leverage move.

The default instinct — buy or build a better model — is a real lever with an honest, limited reach.

Stamp unverified and AI-generated records so people and systems down-weight them instead of inheriting them as fact.

Write less and keep less: the least data that does the job is the least there is to leak, to contaminate, and to purge later.

Govern the sideways pathways — operator-to-operator forwarding, model-to-model hand-offs, and store-to-store replication — that multiply everything else.

An independent second model across the first's outputs — correlated errors surface as disagreement instead of repeating silently across every case.

Check a downstream copy against its source before anyone acts on it — so a mistake written once isn't actioned everywhere the copy lands.

Clean the record system by reading what you remove — deleting unread does not reduce contamination, it concentrates it.

An automated gate on model output before it reaches people is a ceiling, not a substitute — hold the flow, but never trust it to clear the errors.

Restrict what the model can retrieve to a vetted corpus — a bound on the contamination that open auto-retrieval would carry back in, not a scrub.

A content-aware gate on a replication pathway that holds downstream copies until they are reconciled against source — before they drive enforcement.

Spend scarce verification where stakes are highest: mandatory correction plus ground-truth checks for the high-stakes tier.

Require verified sign-off before anything enters the official record — govern the write, not just the read.

Train people and agents to prompt without leading — cutting the loop where the user's belief manufactures its own confirmation.

A scheduled step — recertification, blind re-checks, unassisted rotations — that stops the quiet drift toward rubber-stamping.

Fund continuous learning about what the AI deployment is actually doing across the sociotechnical system, so a limited budget can target hidden dynamics and real failure modes instead of guessing.

Make challenge a scheduled role — rotating second looks, a named devil's-advocate, blameless flagging — so peer checking survives the social pressure that erodes it.

Teach everyone who touches the system what it is and isn't — calibrated trust plus taught boundary rules, closing a documented training gap.

Train staff to actually check AI output — verification as a taught skill, deepest where checking is hardest: conceptual errors.

A pre-authorized clamp-down that fires when a measured signal crosses a threshold — the control Robodebt never had.

Correction effort that rises when observed error rises, and relaxes when it subsides — oversight as a thermostat, not a setting.

No data pathway exists until someone with authority approved it — the general edge-level access control.

Standing review on a mandated schedule — boards, audits, retrospectives — modeled on the actors who actually ended the documented failures.

Procurement as governance: transparency, evaluation access, and exit terms decided while the institution still has leverage.

A formal pre-deployment authorization step — honestly framed: it changes who may act, not how the system behaves.