Having worked extensively with computer vision models for our interview analysis system, this incident highlights a critical challenge in AI deployment: the trade-off between false positive rates and detection confidence thresholds. We initially set our confidence threshold at 0.85 for detecting inappropriate objects during remote interviews, but found this led to ~3% false positives (mostly mundane objects like water bottles being flagged as concerning).

We solved this by implementing a two-stage verification system: initial detection runs at 0.7 threshold for recall, but any flagged objects trigger a secondary model with different architecture (EfficientNet vs ResNet) and viewpoint analysis. This reduced false positives to 0.1% while maintaining 98% true positive detection rate. For high-stakes deployments like security systems, I'm curious if others have found success with ensemble approaches or if they're using human-in-the-loop verification? The latency impact of multi-stage detection could be problematic for real-time scenarios.