How do false positives get corrected in AI filtering?

False positives in AI filtering get corrected through user feedback loops and manual review processes. When users rescue messages from spam folders, this provides training data indicating the filter made an error. Aggregated rescue patterns identify systematic false positive issues.

Sender support channels enable appeals when legitimate mail is incorrectly filtered. Human reviewers examine appealed cases, potentially adding senders to allowlists or adjusting model weights. These manual corrections improve future classification.

Continuous monitoring watches for false positive spikes that might indicate model drift or overfitting. Sudden increases in certain message types appearing in spam trigger investigation. Balancing spam catch rate against false positive rate requires ongoing calibration.