Checklist

AI Validation & Bias Checklist for Operational Models

An interactive, operational checklist teams can use to validate inputs, outputs, fairness, contingency behavior, monitoring, and operational impact before deploying or updating AI models. Records evidence, owners, risk rating, and next steps for auditability and follow-up.

Interactive Tool

AI Validation & Bias Checklist for Operational Models

Use this checklist to verify that an operational AI model meets safety, fairness, and reliability expectations before rollout. Record evidence, owners, and decisions so reviews are auditable. Adapt items to local regulatory or business needs.

Guidance: answer each question, paste links to supporting artifacts, and set a risk rating. Required fields are marked. This form saves a review instance you can revisit for follow-up.

Official model identifier from the model registry (name or ID).
Version, commit, or artifact reference being reviewed.
YYYY-MM-DD (use local date format).
Person performing this validation.
Checks whether inputs have shifted vs training/validation distributions.
Link to drift report, charts, or statistical test outputs.
Confirm coverage across geography, product types, shifts, or customer segments.
Document known gaps and mitigation plans.
Includes label error rates, inter-annotator agreement, and labeling biases.
Paste sample audit results, confusion matrices, or error patterns.
Measure metrics (precision/recall, F1, AUC, MAE, etc.) per segment and compare to thresholds.
Summarize worst-performing segments and their metrics.
E.g., disparate impact, equal opportunity, demographic parity as applicable.
Include values, thresholds, and any mitigation applied.
Check calibration, confidence thresholds, and behavior near decision boundaries.
E.g., human review, reject-to-manual, conservative default action.
Describe the fallback, SLAs, and routing for manual handling.
Specify who reviews exceptions, who has override authority, and training required.
Names, teams, and contact channels for escalation.
Feature importances, example traces, decision logs, or local explanations.
URLs or storage locations for logs, notebooks, or saved explanations.
PII handling, encryption, access controls, and retention policies.
Monitoring should include data drift, concept drift, performance, throughput, and costs, with alert thresholds.
E.g., production error rate, false positive rate, customer impact metrics and thresholds.
Include steps, owners, and maximum acceptable time to mitigate.
List laws, standards, or internal policies considered (e.g., consumer protection, healthcare, finance).
Periodic sampling verifies continued performance and fairness.
Suggested minimum sample size for each audit round.
Person/team accountable for model performance and monitoring.
Operational decision based on checklist results.
1 = Low risk, 5 = High risk
1.0 10.0
List concrete remediation steps, owners, and target dates.
YYYY-MM-DD
Links to model cards, training notebooks, test results, runbooks, or registries.
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