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.
AI Risk Register Entry
Use this form to record one AI-related risk for a research project. Each submission captures the risk, its potential impact, a simple likelihood x severity score, mitigation and monitoring plans, and the person responsible for follow-up.
How to use: Create one entry per distinct risk; keep descriptions concise but specific. Use Likelihood and Severity scales to compute a simple risk score (Likelihood x Severity). Typical actions after saving: review register regularly, export to CSV for reporting, and link high-scoring risks to experiments, approvals, or monitoring workflows.
Example rows:
| Risk description | Potential impact | Likelihood | Severity | Mitigation | Monitoring metric |
|---|---|---|---|---|---|
| Training data imbalance causes subgroup performance gaps | Biased conclusions; harms to underrepresented participants; reputational and regulatory risk | Possible (3) | Major (4) | Collect additional data, apply fairness-aware reweighting, document limitations | Subgroup accuracy gap; fairness metric by cohort |
| Model drift after deployment in new population | Incorrect predictions, invalidated results, wasted downstream experiments | Likely (4) | Moderate (3) | Establish monitoring, retraining triggers; shadow testing | Prediction distribution shift; validation set error |
Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.
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