AI Pilot Safety & Governance Checklist (interactive)

An interactive checklist to ensure AI pilots for vision, PdM, and scheduling include clear allowed actions, human‑in‑loop gates, monitoring and alert thresholds, rollback and retraining triggers, data privacy checks, and governance sign‑off. Saves evidence for review and audit.

Interactive Tool

AI Pilot Safety & Governance Checklist

Use this checklist to ensure AI pilots are safe, auditable, and designed to protect production, people, and product quality. Complete the fields, capture evidence, and save the checklist for governance and post‑pilot review.

Short descriptive name (e.g., Visual Inspection - Bearings)
What decision or task the model should support and expected measurable outcome
Person responsible for running the pilot (name and role)
Pilot start date or date of this checklist
Check to confirm you have a written list of allowed/disallowed model actions
Isolation reduces risk to live operations
Choose the highest-risk mode currently allowed
Who, when, and which decisions require human sign-off
List key metrics (e.g., false positive rate, precision, latency) and alert thresholds
Where alerts go and who responds
Explicit conditions that require immediate rollback or stopping the pilot
When model performance or data drift will trigger retraining
Describe how the system fails safely if the model becomes unreliable
Confirm legal/privacy review and data minimization controls
List datasets, owners, and classification (PII/PHI/Proprietary/Non-sensitive)
Operators and responders trained on new alerts and procedures
Summary of test runs, sample size, error modes, and whether acceptance criteria were met
1 = Not confident, 5 = Very confident
1.0 10.0
Person who approved safety controls
Regulatory or contractual checks performed
Links to datasets, test reports, runbooks, or ticket IDs
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