In conversation analysis, a false positive occurs when an automated model detects a signal (such as an escalation, compliance phrase, interruption, or sentiment shift) even though the call audio and context don’t support it.
False positives matter operationally because they inflate alert volumes, distort trend reporting, and waste analyst and supervisor time reviewing the wrong interactions. They can also lead to incorrect coaching, missed root causes, and reduced trust in dashboards and scorecards.
Managing false positives typically involves tuning thresholds, improving call audio quality and transcription accuracy, and validating detections with spot checks so that workflows focus on truly high-risk or high-impact calls.
