A practical framework for deciding where AI adds value in research and for validating, documenting, and integrating models into reproducible workflows while managing risk.
{ "FormType": "InteractiveForm", "Title": "Model Validation & Reproducibility Checklist", "IntroductionHtml": " Use this interactive checklist to capture evidence that your AI or ML model meets your research's reproducibility and validation needs. Save answers so you can attach them to manuscripts, audits, or...
{ "FormType": "InteractiveForm", "Title": "AI Use Risk Assessment", "IntroductionHtml": " This short assessment helps you classify the risk of a planned AI use and suggests levels of validation and governance appropriate for that risk. Answer honestly—this is for planning, not judgment. ", "SubmitLabel": "Assess...
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.