Practical checklists and governance patterns to manage AI privacy, model risk, explainability, and human oversight.
An actionable, fillable pre-deployment and periodic review checklist to evaluate data provenance, privacy, model risk, explainability, human oversight, monitoring, vendor risk, retention, and incident readiness. Includes a simple risk-tier rubric, owner and review fields, and a notes section so teams can save a documented decision and follow up on mitigations.
An actionable, savable checklist teams can use to assess readiness before introducing AI-assisted tools into workflows. Includes fields to capture approvals, effort estimates, risk scoring, ownership, and notes to support prioritization and follow-up.
A practical, reusable matrix-style checklist teams can use to evaluate privacy, bias, explainability, data minimization, security, human oversight, vendor risk, and operational safety before advancing an assistant or automation from pilot to production. Includes concrete evidence examples, rating guidance, sample mitigations, and suggested production thresholds.
An actionable, evidence-oriented checklist that teams can use to assess AI projects for privacy, fairness, explainability, monitoring, human oversight, vendor risk, and incident readiness. The form captures owners, evidence, priorities, and recommended next steps so teams can operate, track, and improve responsibly.
A practical, action-oriented checklist to evaluate privacy, consent, retention, access, bias, provenance, and operational risks when datasets are repurposed for organizational learning, analytics, or AI. Includes guidance on how to use the checklist, suggested mitigations, and a simple action template for assignment and follow-up.