Assessment & Template

AI Governance — Model Risk & Safety Assessment for Operations

A required, pre-deployment interactive assessment and template to evaluate purpose, data lineage, performance targets, bias and fairness risks, human-in-the-loop controls, failure modes and mitigations, monitoring and retraining needs, gating decisions, and required approvals for operational AI pilots.

Interactive Tool

AI Model Risk & Safety Assessment — Operations

Purpose

This assessment helps teams evaluate model readiness for operational deployment. Use it before any pilot or production rollout to document intended use, risks, mitigations, monitoring needs, gating criteria, and required approvals. Save the completed form to create an auditable record tied to the model.

How to use

Complete each field honestly. Provide links to supporting artifacts (validation reports, datasets, model cards) in the text fields or in your project repository and reference them here. If you select 'Approve with Conditions', list specific gating actions in the acceptance criteria. Teams should retain this assessment alongside model artifacts and monitoring logs.

Unique name or registry identifier for the model (e.g., invoice-risk-v2).
Person or team accountable for model lifecycle, monitoring, and incident response.
Describe what decision or action the model supports, the users or systems that consume it, and the expected outcome.
Where will the model run? (e.g., control room dashboard, frontline mobile app, automated actuator). Include frequency and scale of decisions.
Estimate potential harm or operational impact if the model fails or misbehaves.
Summarize sources, transformations, sampling, and any synthetic data. Reference data catalog entries or dataset IDs if available.
List datasets and their owners. Note any known coverage gaps, labeling methods, or quality issues.
Specify metrics (accuracy, precision, recall, AUC, MAE, etc.) and minimum acceptable thresholds in the operational context.
Summarize offline validation, cross-validation, holdout results, and stress or adversarial tests. Include link or reference to full validation report.
Describe groups that could be disadvantaged, known biases, and steps taken to detect or mitigate bias.
If yes, describe the human role, responsibilities, SLAs, and escalation path.
Describe required approvals, review frequency, override mechanisms, and training for human reviewers.
List ways the model could fail in production and the observable symptoms you would expect to see.
List technical, process, or human controls (e.g., thresholds, safe-fail modes, circuit breakers, fallbacks, guardrails).
Select monitoring types you will implement. Add details in the monitoring details field.
Describe metrics to monitor, alert thresholds, where alerts will be routed, and runbook references.
How and when the model will be retrained, redeployed, and validated.
Clear, testable criteria that must be met before deployment (e.g., metrics thresholds, monitoring implemented, approvals acquired).
Select the recommended gating outcome based on the assessment.
After mitigations, estimate the remaining risk level.
1.0 10.0
Team judgment of operational readiness considering all factors.
1.0 10.0
Identify which roles must sign off before production deployment.
Names, roles, and contact info for people to notify for incidents or changes.
Links to model card, validation reports, dataset registry entries, runbooks, or ticket numbers.
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