AI Content Moderation with Explainable Policy Enforcement
Platforms need to moderate user content at scale while avoiding false positives, maintaining creator trust, and ensuring decisions are explainable.
Das Problem
AI classification outputs are validated by Corules before moderation actions execute. High-confidence violations are actioned immediately. Low-confidence or edge cases route to human reviewers. Creator-facing explanations reference the specific policy category without leaking internal model details. Every decision is logged with the classifier version and policy version.
So löst Corules es
ESCALATE: Violation confidence 0.65 below auto_action_threshold 0.85. Routing to human moderator.
Richtlinienbeispiel
// Content moderation policy (CEL)
context.violation_confidence < params.auto_action_threshold
? "ESCALATE"
: context.violation_category in params.zero_tolerance_categories
? "BLOCK"
: "ALLOW"Integrationsoptionen
Frequently Asked Questions
How does this prevent false positives?
The auto_action_threshold parameter sets the confidence floor for automatic action. Below that threshold, decisions escalate to humans rather than firing incorrectly.
What do creators see when their content is actioned?
The response includes a creator-safe explanation that cites the policy category (e.g., 'financial advice') without exposing internal confidence scores or model details.