CrisisGuard: Crisis Detection and Protection Architecture for Mental Health AI Systems

CrisisGuard is a mental health AI safety protection system fine-tuned on RoBERTa, serving as a production-grade safety barrier to provide crisis detection and intervention capabilities for mental health chatbots. Its core advantages include reducing the false negative rate from over 97% to 1.47% (a 98.5% reduction), with an end-to-end response latency of only 213ms, effectively addressing the problem of missed crisis signals in traditional solutions.

The application of large language models in mental health support systems carries fatal safety risks; failing to timely identify user crisis signals (such as suicidal ideation) can lead to severe consequences. Traditional keyword filtering can only capture 3% of self-harm intentions and 2.8% of suicidal ideation, with a false negative rate exceeding 97%. Mental health AI safety requires deep semantic understanding, which is different from the pattern matching used in general content moderation.

CrisisGuard acts as a front-end safety barrier, consisting of four core modules:

1. Crisis Classifier: Fine-tuned on RoBERTa, classifying into 5 severity levels (0-normal to 4-suicidal ideation);
2. Safety Router: Blocks LLM requests when Level 3/4 high risks are detected, returns intervention responses, and records the event;
3. Generation Backend: Forwards only safe requests to Groq-hosted Llama-3;
4. Monitoring System: Real-time recording of predictions, confidence levels, and anomalies, supporting auditing and improvement.

Training Strategy: RoBERTa-base is fine-tuned on 160 labeled data samples, using stratified 5-fold cross-validation. Performance Comparison:

• Self-harm detection recall: 100% vs keyword baseline of 3% (33x improvement);
• Suicidal ideation recall:97.2% vs 2.8% (35x improvement);
• False negative rate:1.47% vs over97% (66x reduction);
• Latency:213ms vs keyword 50ms (only 163ms increase). Compared to general moderation APIs (recall rate 12.5%-45.3%), the dedicated model has significant advantages.

Integration Solution: Provides a Python API, which can be integrated with just a few lines of code; Deployment Mode: Microservice architecture (FastAPI classifier + Spring Boot main API) ensures high availability and low latency; Intervention Response: Returns preset messages when high risks are detected, including professional support such as emergency hotlines (e.g., 988) and SMS help (text HELLO to 741741).

Training Data: 160 synthetic + expert-reviewed samples covering five severity levels, requiring ethical application for download; Transparency: Decision records are auditable, with classification confidence levels and routing basis clearly traceable, ensuring interpretability and the possibility of human intervention.

General AI safety solutions have limited effectiveness in high-risk vertical domains (such as mental health), requiring integration of domain knowledge, professional data, and targeted optimization. CrisisGuard is open-sourced to provide a reference for developers; its front-end classifier + router + monitoring architecture can be extended to sensitive scenarios like children's education and elderly care.

CrisisGuard is a deployable safety infrastructure for mental health chatbots, exchanging a 163ms latency cost for over 30x improvement in safety. In today's era where AI is permeating the mental health field, such specialized safety solutions are a must, providing a practical example for responsible AI deployment—every timely identified crisis signal could save a life.