Checklist

AI Prototyping & Safety Checklist

An actionable, storable checklist to run practical safety, privacy, fairness, and operational checks while rapidly prototyping AI features. Mark items, add evidence, assign owners, and record readiness decisions.

Interactive Tool

AI Prototyping & Safety Checklist

Use this checklist when designing, building, or testing AI prototypes. Mark each item, add notes or evidence, assign an owner, and record an overall readiness and risk assessment. Saving responses creates a reusable record for reviews, audits, and follow-up actions.

Who is responsible for this prototype's safety and compliance (name, role, or team).
YYYY-MM-DD
Has data lineage, consent, minimization, access controls, and storage been reviewed?
List datasets, data sources, consent status, and any mitigation steps.
Were target groups, sampling, metrics, and potential disparate impacts reviewed?
Describe tests run, slices reviewed, and any corrective actions.
Is there a minimum acceptable performance or safety threshold, and does the prototype meet it?
Record metric names, thresholds, test datasets, and results.
Does the system degrade safely or hand off to a human when confidence is low or failures occur?
Describe fallback logic, human-in-the-loop steps, and examples.
Are logs, telemetry, alerts, and monitoring defined to detect failures, drift, or misuse?
List signals to monitor, retention, and alert thresholds.
Are users informed and consent captured when required by privacy or ethics considerations?
Provide links or text of disclosures and consent records.
Is there a clear rollback path, incident response, and communications plan for problems?
Outline steps, who executes them, and how users will be notified if needed.
Have external models, APIs, or datasets been reviewed for license, provenance, and known issues?
Estimate the prototype's residual risk after mitigations.
Based on the above checks, is it acceptable to test with real users?
List follow-up actions, owners, and deadlines.
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