How Do We Use AI Responsibly in Research?

A practical framework for deciding where AI adds value in research and for validating, documenting, and integrating models into reproducible workflows while managing risk.

How Do We Use AI Responsibly in Research?
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AI Risk Register for Research Projects

Interactive template to log AI-specific risks (bias, privacy, misuse, model drift, interpretability, regulatory) with likelihood, severity, mitigations, monitoring metrics, owner, review cadence, and examples. Saves structured entries for later export, dashboarding, and follow-up.

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ML Model Card & Validation Checklist

A practical, fillable model card template with a detailed validation checklist covering dataset provenance, training and evaluation procedures, subgroup performance, robustness checks, intended use, limitations, reproducibility requirements, versioning, and monitoring.

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