Severity and uncertainty
Two separate axes for rating an AI incident: how serious the effect is, and how well it is understood. Something can be minor but unexplained, or serious and well understood.
When an AI system behaves unexpectedly, teams tend to use one scale, often a single severity level. That conflates two questions. Severity asks how serious the effect is: what was touched, who was affected, how reversible it is. Uncertainty asks how well the event is understood: do we know what happened, why, and whether it can recur?
The axes are independent. A minor-looking anomaly that nobody can explain may deserve more investigation than a serious but fully understood failure. A serious and uncertain incident needs both urgent containment and careful preservation of evidence.
Using both
Record them separately in the observation record and revisit uncertainty as the explanation improves. The effect side connects to blast radius. Rating this way avoids the two common mistakes: calling every odd output a crisis, and ignoring an unexplained one because it looks small.
Read more in The first AI incident report should be incomplete.
Related terms
Observation vs. explanation
The split between recording what happened in an AI incident (observation) and the current theory of why (explanation), so early facts are preserved while explanations are revised.
AI incident reporting
Recording anomalous AI behaviour as evidence at the moment it is observed, before root cause is known, keeping what was seen separate from the later explanation of why.
Blast radius
How far the damage of a failure can spread: which data, systems, people and money an AI action can affect if it goes wrong. Smaller radius means safer delegation.