Checklist

AI Pilot Safety & Ethics Checklist

Safety and ethical guardrails for AI pilots including human-in-loop requirements, false-positive/negative risk analysis, data privacy checks, monitoring, and stakeholder communication — now as a structured checklist teams can complete and save.

Interactive Tool

AI Pilot Safety & Ethics Checklist

Use this checklist to assess and document safety, ethical, regulatory, and operational guardrails before, during, and after an AI pilot. Complete each item, record evidence or mitigation steps, assign owners, and save the results so your team can track issues, approvals, and readiness to scale.

Short descriptive name (e.g., 'Inbound QC Visual AI Pilot').
Optional internal tracking ID.
What processes, lines, products, or decisions does the pilot cover? What are the measurable objectives?
High-level assessment combining safety, quality, regulatory, and reputational risk.
1.0 10.0
List top risks, estimated likelihood, impact, and initial mitigations. Attach deeper risk register elsewhere if needed.
Will humans review or be empowered to override AI outputs?
Who makes the final decision? What training/capability is required?
If the AI fails, produces low confidence, or returns unexpected outputs, is there a safe fallback?
Describe stop-gaps, degraded-mode operation, manual takeover, or hold-for-human actions.
Which data is used, where does it come from, and how is it preprocessed? Include sampling frequency, retention, and access controls.
Includes PII, health data, financial, or other regulated fields.
If yes, briefly describe the technique. If no, explain why and how privacy is protected.
Describe hashing, tokenization, aggregation, or minimization approaches and any re-identification risk mitigation.
Numeric thresholds for accuracy, precision, recall, false positive/negative rates, latency, and business KPIs.
List each metric, acceptance value, measurement method, and required sample size or confidence interval.
Describe safety, quality, cost, or operational consequences if the model falsely flags an event.
Describe consequences if the model misses a true event.
Are metrics, drift detection, model versioning, and logs planned for ongoing monitoring?
Which metrics will be tracked? What triggers alerts and who is notified? Include data retention and access for audits.
Who must be informed about pilot results, incidents, or pause/stop decisions? Include cadence and channels.
List applicable regulations, standards, or customer contractual clauses the pilot must satisfy.
Have frontline operators and supervisors been trained on system behavior, limitations, and override procedures?
Where are the training slides, attendance records, and quick reference guides stored?
Is there a documented plan for investigating and remediating model errors, data leaks, or safety incidents?
Who leads an investigation? How are customers/employees informed? Time-to-containment goals.
Person accountable for pilot safety and ethics.
YYYY-MM-DD preferred.
Periodic review cadence (e.g., weekly, monthly).
Track pilot approvals.
List outstanding mitigation tasks, owners, and due dates.
Judgment of whether the pilot is ready to scale given safety, ethics, performance, and ops readiness.
1.0 10.0
Quick yes/no decision by governance or pilot owner.
Links to model cards, data dictionaries, validation reports, audit logs, and storage locations.
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