/fɔːls ˈpɒzɪtɪv/ • Type I Error
A false positive occurs when an AI moderation system incorrectly identifies safe content as harmful. For example, flagging a medical image as nudity, or marking artistic content as violence. False positives frustrate users and can damage platform trust.
In statistical terms, a false positive is a Type I error - rejecting a true null hypothesis (the content is actually safe).
High false positive rates lead to user frustration, content creator churn, appeals overload, and loss of platform trust. Balancing false positives against false negatives is a key challenge in moderation.
Better AI models, adjustable confidence thresholds, human review for edge cases, appeals processes, and context-aware systems all help reduce false positives while maintaining effective moderation.