[Crisis AI #1] Minimizing FNR in Suicide NLP & Bayes Distortion
In consumer chatbots or product recommendation engines, an artificial intelligence error rate of 5% is an acceptable trade-off or a minor nuisance. In AI systems engineered for suicide crisis monitoring and psychiatric emergency triage, a single False Negative (missed crisis) is catastrophic.
When an individual in acute despair reaches out or journals about ending their life, machine learning pipelines cannot afford to hide behind high overall accuracy or balanced macro-F1 averages.
Today, we officially inaugurate a major technical production series: [High-Stakes Crisis AI: Building a Precision Suicide & Mental Health NLP Detection Pipeline].
This comprehensive devlog documents the exact mathematical hurdles, algorithmic trade-offs, and runtime bugs encountered while engineering Maeumjigi, an enterprise-grade multimodal crisis detection system. In this Episode #1, we tackle the foundation of the architecture: sentence-level deep NLP crisis triage under extreme class imbalance, where active suicidality accounts for a mere 0.16% of daily discourse.