Problem it solves
Low-confidence output is presented as confident output. Users make decisions based on AI responses without knowing the model's uncertainty level.
When to use
Whenever model confidence drops below the operator-configured threshold on a consequential output.
When not to use
For confidence thresholds on low-stakes, exploratory output. Not every low-confidence response requires a governance event.
Governing principle
When confidence falls below threshold, the UI must surface a visual breach state, name the source of uncertainty, and offer concrete recovery options. Auto-proceeding on low-confidence output is not acceptable.
Required Components
Interaction Flow
Output generated
The model produces a response and returns its confidence score.
Threshold check
The system compares the confidence score against the operator-configured threshold.
Breach state surfaces
If the score is below threshold, the Confidence Threshold Warning activates a visual breach state on the output.
Uncertainty source named
The Uncertainty Indicators component identifies what is uncertain: data recency, conflicting sources, ambiguous input, or model limitation.
Recovery options presented
The user can send for human review, adjust the threshold for this output, or override with documented justification.
Decision logged
The user's choice is written to the governance record, including what threshold was active and what justification was provided.
Governance requirements
Threshold breach events are governance events. Each breach must be logged with the confidence score, threshold at time of breach, uncertainty source identified, and the user's recovery action.
Accessibility notes
Threshold breach states must be announced via role="status" (not role="alert" unless the breach blocks the workflow). Visual breach indicators must pass WCAG contrast requirements.