Problem it solves
AI outputs involving demographic data or low-representation categories are presented without surfacing known bias risks, putting users and organizations at regulatory and ethical exposure.
When to use
When output involves demographic data, low-representation categories, high-variance populations, or any domain known to carry model bias risk.
When not to use
For outputs with no demographic or statistical dimension. Bias surfacing should not appear for irrelevant content — alert fatigue will cause users to dismiss legitimate warnings.
Governing principle
Bias surfaces are governance events, not warnings. They must be logged, must offer escalation paths, and must never be auto-dismissed.
Required Components
Interaction Flow
Output involves a risk domain
The system detects that the output involves demographic data, a low-representation category, or a known bias risk signal.
Bias alert surfaces
The Bias Check Prompt component activates on the output, identifying the specific risk type and source.
Risk explained
The interface explains the nature of the statistical risk: what population is affected, what the model's known limitation is, and what downstream decisions might be impacted.
Escalation path offered
The user can send the output for human expert review, flag it for compliance review, or proceed with documented acknowledgment of the risk.
Decision documented
The user's choice is logged as a governance event with the risk type, the output affected, and the action taken.
Governance requirements
Bias events must be logged regardless of user action. The log must include the risk type, the output or decision affected, and the escalation or acknowledgment action taken.
Accessibility notes
Bias alerts must use role="status" for advisory alerts and role="alert" for high-severity bias risks. Risk explanations must be readable by screen readers in their full form.