Practical guidance, checklists, and a template to prototype AI safely—minimizing bias, privacy and safety risks while preserving user trust.
Interactive checklist to ensure safe, ethical, and compliant rapid AI prototyping with saved responses, evidence links, and action items for rollback and monitoring plans.
An interactive, safety-first checklist to run, record, and sign off AI prototypes. Capture risk assessments, data protections, evaluation metrics, operational controls, stakeholder communication, and a one-page safety brief. Save records for review, audit, and reuse.
An actionable, storable checklist to run practical safety, privacy, fairness, and operational checks while rapidly prototyping AI features. Mark items, add evidence, assign owners, and record readiness decisions.
A practical, step-by-step operational checklist to run low-risk AI prototypes that account for data provenance, consent, bias, privacy, explainability, monitoring, and rollback planning. Includes quick triage, acceptance criteria, monitoring signals, and short examples for classification, recommendation, and generative prototypes.
A practical, step-by-step assessment template to identify, rate, and mitigate model safety, fairness, privacy, robustness, and operational risks during prototyping and before any production decision. Includes guided prompts, a harm-surface mapping approach, suggested test cases, monitoring and rollback guidance, and documentation checklists.
An operational interactive pre-check teams can complete before launching AI prototypes. Collects yes/no responses, evidence notes, risk flags, ownership, and suggested next steps so teams can act, escalate, and track follow-up.
An actionable, interactive checklist to screen experiments, prototypes, and pilots for ethical, fairness, privacy, safety, environmental, and regulatory concerns. Designed to capture decisions, owners, residual risks, and recommended mitigations so teams can move faster with documented safeguards.