Checklist

AI model risk, validation & documentation checklist

An actionable, recordable checklist to evaluate AI model readiness for research use. Prompts reviewers to capture intended use, failure tolerance, data provenance, bias and robustness checks, validation results, interpretability and uncertainty practices, documentation and reproducibility steps, monitoring and retraining plans, approvals, and residual risk.

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AI model risk, validation & documentation checklist

Use this checklist to evaluate whether an AI model is ready for use in research workflows. For each item, select yes/no or choose the appropriate response, provide evidence or links where requested, assign an owner, and rate residual risk. Saving the completed checklist creates an auditable record to support reproducibility, approvals, and ongoing monitoring.

Tips: Attach or link model artifacts and datasets in your registry, be explicit about failure tolerance, and specify quantitative retraining triggers where possible.

Describe the intended research use-case, decisions the model will support, and expected benefits. Include scope, populations, and constraints.
Describe potential failure modes, their likely consequences, and what level of error or false result is tolerable for each case.
Has the provenance and lineage of training data been reviewed (source, collection method, preprocessing)?
List known or suspected biases in the data and steps taken to detect or mitigate them. Reference bias checks or fairness metrics used.
Has a held-out validation dataset and at least one external test (different dataset, lab, or time period) been defined?
Summarize validation results (metrics, confidence intervals, sample sizes) and whether they meet pre-specified acceptance criteria. Cite thresholds and how they were chosen.
Select the types of robustness checks performed.
Has the model's interpretability been assessed and documented (feature attributions, surrogate models, rules)?
Describe interpretability methods used and any notable failure / misleading explanations observed.
Is model uncertainty estimated (e.g., calibrated probabilities, prediction intervals) and integrated into decisions or reporting?
Is there a model card or equivalent documentation including dataset descriptions, training procedure, hyperparameters, intended use, and limitations?
Provide a link, DOI, or artifact/model registry ID where documentation and artifacts are stored.
Record model version, code commit hashes, hyperparameters, environment (containers/conda), and the exact steps to reproduce training and evaluation.
Describe monitoring metrics (performance, data drift), detection methods, alerting thresholds, and who is responsible for review.
Specify quantitative triggers for retraining or human review (for example: metric drop > X%, data drift score > Y), and the escalation path.
List applicable regulations, consent or privacy constraints, data-sharing limits, and any ethical review outcomes.
Names, roles, and signatures/IDs of people who have reviewed and approved the model for use in this research context.
Choose the overall residual risk after mitigations have been applied.
Assess whether the model is ready to be used in research workflows.
List links or IDs for datasets, evaluation notebooks, model artifacts, evaluation reports, and registry entries.
Any other notes, mitigation actions, or follow-up tasks.
Date of this checklist review (YYYY-MM-DD).
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