Responsible AI
Human Review & Release Checklists
Human review is a control for uncertain, high-impact, or low-confidence cases. A release checklist makes AI risk explicit before users discover it the hard way.
- Escalation defines safe failure
- Reviewers need evidence, not just output
- Review data improves the system
- Checklists prevent demo blindness
- High-stakes domains need stronger gates
- Review must have authority
| Area | Question |
|---|---|
| Task fit | Why is AI needed instead of deterministic code? |
| Eval | Do we have representative and adversarial examples? |
| Grounding | Can outputs be tied to evidence where needed? |
| Safety | What should the system refuse or escalate? |
| Privacy | What enters prompts, logs, indexes, and review queues? |
| Security | Can untrusted text influence tools or leak data? |
| Fairness | Which slices must be checked? |
| Operations | Can we monitor, roll back, and audit? |
| Human review | Who reviews uncertain/high-impact cases? |