Section 169 · Chapter 20, The Practical Playbook
Failure Modes and Fail-Safe AI
The safest AI systems are designed so likely failures become bounded, visible, reversible, and boring instead of catastrophic.
What to do
- Do not only test happy paths.
- Design tests around control points: abstention, escalation, permission checks, rate limits, sandboxing, reversibility, auditability, and human override.
- Define runnable checks that exercise fail-safe and failure modes fail safe.
Evidence to preserve
- Include missing documents, stale policies, ambiguous user intent, prompt injection, low-confidence retrieval, tool failures, permission boundaries, malformed inputs, adversarial phrasing, and requests where the correct outcome is no action.
- Preserve the inputs, versions, configurations, raw outcomes, and results for fail-safe, failure modes fail safe needed to reproduce work on Failure Modes and Fail-Safe AI.
- Report results for fail-safe, failure modes fail safe by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, combine AI evals with safety engineering practices such as hazard analysis, fault-tree analysis, threat modeling, incident response, quality gates, and post-release monitoring. Design tests around control points: abstention, escalation, permission checks, rate limits, sandboxing, reversibility, auditability, and human override. A model score is not enough if the system architecture lets one bad output cause unbounded harm.
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Cite this page
Jason Arbon. "Failure Modes and Fail-Safe AI." Testing AI Knowledge Edition, section 169.
https://jarbon.ai/testing-ai/knowledge/ch169-failure-modes-fail-safe.html