Checklist

AI Governance & Responsible Use Checklist for Operations

An interactive, evidence-oriented checklist to validate AI/ML systems for operational use. Covers problem framing and risk, data provenance, performance validation, bias and fairness, human-in-the-loop requirements, rollback and safety controls, monitoring metrics and alert thresholds, documentation, and an operational sign-off template.

Interactive Tool

AI Governance & Responsible Use Checklist for Operations

This checklist helps operational teams validate and document responsible AI practices before and during deployment. Use it to record evidence, define acceptance criteria, and create an auditable trail for decisions that affect safety, compliance, or customer outcomes.

Describe the operational decision the model will support, who acts on the output, and expected benefits.
Select the highest applicable level. High or critical systems require stronger controls and approvals.
Check all evidence items you have confirmed. Attach references in Notes.
Include links or identifiers to dataset records, version IDs, or data catalog entries.
Record the primary metric used during training (e.g., accuracy, F1, MAE). Enter numeric value. Note units if applicable.
Record the primary metric on validation data used to tune the model.
Record the primary metric on the held-out test set used for release decisions.
State clear numeric criteria for go/no-go (e.g., test accuracy >= 0.92, false positive rate <= 0.05).
Yes if differences between train/validation/test were analyzed and acceptable explanations exist.
Has the model been evaluated for disparate impact across relevant groups?
Check attributes audited for fairness. Use Notes to list additional attributes.
Yes if mitigation strategies (rebalancing, constraints, post-processing) have been implemented and tested.
Summarize any detected disparities, chosen mitigations, and residual risks.
Does the operational design include a human reviewer, override process, or approval gate?
List roles (by title) who will review or act on model outputs and decision thresholds for escalation.
Describe the rollback plan, fail-safe modes, and how to stop or revert automated actions if needed.
Yes if a rollback or failover has been executed in a test environment and results logged.
Enter the numeric threshold that will trigger investigation (e.g., accuracy falls below 0.90).
Record the latency threshold that should trigger an alert if exceeded.
Yes if who-to-notify and how to respond are recorded for each alert.
Paste IDs, links, or storage paths to the model artifact, evaluation reports, and runbooks.
Person responsible for day-to-day operation and monitoring of this model.
Owner confirms all required checks are complete and documented. Sign-off indicates readiness to operate under the documented controls.
Use ISO date (YYYY-MM-DD) or local preferred format.
Record any residual concerns, planned experiments, monitoring cadence, or mitigation timelines.
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