Section 144 · Chapter 18, Embodied and Long-Running AI Systems
Testing Dangerous Physical and Embodied AI
When AI can move matter, spend money, unlock doors, steer vehicles, or operate tools, testing must treat action as risk.
dangerous physical embodied
What to do
- Start with the action inventory.
- Test permission boundaries.
- Use physical rate limits and hard constraints.
- Do not rely only on the model's judgment when a mechanical limit, spend cap, geofence, speed limit, or emergency stop can reduce harm.
- Test compounded actions.
Evidence to preserve
- Report false positives, false negatives, and escalation rates by meaningful slices, not only as one aggregate number.
- Preserve the inputs, versions, configurations, raw outcomes, and results for dangerous physical embodied needed to reproduce work on Testing Dangerous Physical and Embodied AI.
- Report results for dangerous physical embodied by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Physical AI testing should include hazard analysis, fault-tree analysis, misuse cases, safety envelopes, runtime monitors, independent interlocks, audit logs, staged rollouts, near-miss analysis, and adversarial action-chain testing.
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Cite this page
Jason Arbon. "Testing Dangerous Physical and Embodied AI." Testing AI Knowledge Edition, section 144.
https://jarbon.ai/testing-ai/knowledge/ch144-dangerous-physical-embodied.html