Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbots

TL;DR

This work tackles safety gaps in mental-health LLMs by introducing domain-specific Constitutional AI (CAI) trained with tailored mental-health guidelines. It derives domain-adapted constitutional principles, compares them against general ethical baselines, and demonstrates that principled, self-critique–driven training with SFT and RLAIF substantially improves crisis handling, guideline adherence, and resource provision. Importantly, a 1B parameter model trained with specific principles can outperform a larger unprincipled model, highlighting efficiency and deployment viability in resource-constrained healthcare settings. The findings advocate for domain-aware CAI as a practical pathway to safer, more reliable AI-assisted mental health care and point to the need for regulatory-informed principle standardization and dynamic updates as guidelines evolve.

Abstract

Mental health applications have emerged as a critical area in computational health, driven by rising global rates of mental illness, the integration of AI in psychological care, and the need for scalable solutions in underserved communities. These include therapy chatbots, crisis detection, and wellness platforms handling sensitive data, requiring specialized AI safety beyond general safeguards due to emotional vulnerability, risks like misdiagnosis or symptom exacerbation, and precise management of vulnerable states to avoid severe outcomes such as self-harm or loss of trust. Despite AI safety advances, general safeguards inadequately address mental health-specific challenges, including crisis intervention accuracy to avert escalations, therapeutic guideline adherence to prevent misinformation, scale limitations in resource-constrained settings, and adaptation to nuanced dialogues where generics may introduce biases or miss distress signals. We introduce an approach to apply Constitutional AI training with domain-specific mental health principles for safe, domain-adapted CAI systems in computational mental health applications.

Figures (3)

• Figure 1: Three types of model: model without CAI alignment, model with vague/general principle, model with specific domain related principle
• Figure 2: Individual Guideline Performance Analysis
• Figure 3: Radar Chart Comparison: Specific Principles Model, Vague Principles Model, and Ablation Model