Why rare failures matter for the assurance of embodied AI
How smarter testing and statistical certification can strengthen safety evidence
Embodied AI systems – such as autonomous vessels, drones, and robots – do not live in datasets or benchmarks. They move through the world, interact with uncertain environments, and make decisions that affect people, assets, and operations in real time.
In safety-critical domains, the issue is rarely whether a system works in the average case. Failures tend to hide in the ‘tails’: unusual combinations of initial conditions, sensor noise, or environmental states that rarely arise under nominal operation, but carry severe consequences when they do.
When failures are rare, standard testing can easily spend most of its budget on uneventful scenarios and still say very little about how likely failure really is. What matters for assurance is not simply how many scenarios we test, but whether testing produces meaningful, defensible evidence about how likely failure is under realistic operating conditions.