Checklist

Model Validation Checklist & Test Suite (Interactive)

An actionable, interactive checklist and test-suite template to standardize model validation across data, performance, robustness, fairness, explainability, and approval. Capture results, evidence, risk rating, and approver sign-off for auditability and repeatability.

Interactive Tool

Model Validation Checklist & Test Suite

Model Validation Checklist & Test Suite

Use this interactive checklist to capture repeatable, auditable validation work before approving a model for production. The form groups validation into practical sections: data checks, performance, robustness, fairness, explainability, and final approval. For each test, record whether it was performed, the result, evidence links or artifact IDs, and comments. Save the form to attach results to the model record.

Adapt fields to your domain and regulatory needs. This template is a starting point — expand subgroup checks, automated metric reporters, or dataset snapshots as required.

Stable identifier, version, or hash for the model under validation (required).
Person or team responsible for running these tests (name or team alias).
Date of validation (YYYY-MM-DD).
(Section header — no input required)
Did you check schema, types, and required fields for training and serving datasets?
Choose the result and provide evidence below.
Link to dataset snapshot, schema diff, or artifact that proves the check.
Checked ranges, missing values, outliers, and plausibility for key features.
Summarize any anomalies and remediation taken.
(Section header — no input required)
Report key metrics on holdout / test sets (e.g., accuracy, precision, recall, AUC, RMSE). Include values and the evaluation dataset ID.
Select if model meets predefined performance thresholds on the specified metrics.
Document known limitations, dataset shifts, label quality concerns, or required monitoring.
(Section header — no input required)
Were simple adversarial, noise, or perturbation tests applied to inputs to check fragility?
Tests using OOD datasets or detection checks to observe behavior on unfamiliar inputs.
Summarize failure modes, percent degradation, or mitigation strategies (e.g., reject-on-uncertainty).
(Section header — no input required)
Were subgroup analyses run for protected attributes and operationally important cohorts?
List subgroups (e.g., age brackets, regions, device types) and why they matter.
Report subgroup metrics (or attach a file/link) and note any unacceptable disparities and mitigation steps.
(Section header — no input required)
Were feature importances, SHAP/LIME, counterfactuals, or example-based explanations reviewed for sensibility?
Select the explanation methods applied.
Describe any surprising feature importance results, proxy variables, or suspicious behavior.
(Section header — no input required)
Rate operational risk if deployed (1 = Low risk, 5 = Very high risk).
1.0 10.0
Select final recommendation.
If approval is conditional, list required tests, monitoring thresholds, retraining cadence, or guardrails.
Name of person who signs off on this validation record (role/title preferred).
Typed name or reference to electronic signature as allowed by your governance process.
Any other observations not captured above (e.g., dependencies, config, data drift concerns).
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