Mental & Behavioural Health AI
Building AI that supports mental health without causing harm
Industry Challenge
Mental health AI — from crisis detection tools to therapy assistants and mood prediction models — carries the highest stakes in healthcare AI. Errors can cost lives. These models require annotators with genuine clinical sensitivity, careful training in safe messaging, and rigorous red-teaming to identify harmful outputs before deployment.
How SCILabel Serves This Industry
Data Collection
We source anonymised mental health assessment records, crisis helpline transcripts (consented and de-identified), psychotherapy session summaries, and patient-reported outcome measures from academic clinical psychology and psychiatry research partners.
Data Annotation & Labeling
Our most carefully selected clinical workforce annotates crisis and risk signals in text, sentiment and affect labels, clinical severity classifications (PHQ-9, GAD-7, Columbia Suicide Severity Rating Scale), and therapeutic dialogue quality labels. All annotators complete mandatory safe messaging training before accessing this data category.
Data & Model Evaluation
Track 3 (RLHF) and Track 5 (AI Safety) evaluators assess mental health AI outputs for clinical safety, safe messaging compliance (following AFSP/Samaritans guidelines), de-escalation quality, and crisis escalation accuracy. Red-team testers submit adversarial inputs mimicking crisis scenarios.
Annotation Types & Formats
- Crisis signal detection in text: suicidal ideation, self-harm risk, acute distress
- Clinical severity classification: PHQ-9, GAD-7, C-SSRS
- Safe messaging compliance labeling on AI-generated responses
- Sentiment, affect, and emotional valence annotation
- Therapeutic alliance and empathy quality scoring
Specialist Workforce Tracks
Track 3 (RLHF) and Track 5 (AI Safety): Psychiatric Nurses, Counsellors-in-training, Clinical Researchers, Public Health Professionals — all completing mandatory safe messaging training.