Responsible AI Guardrails — Quick Checklist

Apply this checklist before any live exposure. Ticking these boxes doesn’t guarantee safety, but it significantly lowers risk and clarifies remaining work.

1. Clear purpose and owner. A named owner and a measurable value hypothesis are documented.
2. Minimal scope and human oversight. The pilot limits the scope of automated decisions and keeps humans in the loop for exceptions.
3. Data privacy & access controls. Sensitive data is identified; access is restricted and logged; de-identification is used where possible.
4. Data quality review. Known gaps, label quality, and representativeness have been assessed.
5. Bias and fairness checks. Basic fairness tests run on key groups; risky disparities are documented with mitigation plans.
6. Explainability & transparency. The team can explain how the system is used and what types of errors to expect.
7. Monitoring & rollback triggers. Metrics, alert thresholds, and an explicit rollback procedure are defined and tested.
8. Legal & compliance review. Relevant regulations, contracts, or vendor terms have been considered; Legal/Privacy has reviewed if needed.
9. Security & Third-party risk. Model and data access controls are in place; third-party model risks are documented.
10. Documentation & handoff. A short runbook records model version, data, owners, and the decision policy for future teams.

Use this checklist as a go/no-go gate before expanding the pilot or exposing it to customers.