Title: Ethical Dialogue Modeling: Dual-Phase Prompt Design for Safety-Constrained Mental Health Language Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Therapeutic Dialogue Systems, Large Language Models, Safety-Constrained Prompt Design, Contextual Risk Stratification, Therapeutic Alliance Modeling, Affective State Inference, Clinical AI Safeguards

Abstract: We introduce Ethical Dialogue Modeling (EDM), a two-stage architecture for safety-constrained clinical mental health support. In EDM, natural-language inputs are first translated into structured semantic and risk representations that expose affective intensity, therapeutic needs, and graded hazard estimates. These representations then condition a protocol-aware generation stage that enforces domain-specific safeguards and ethical constraints so that responses remain therapeutically appropriate while avoiding potentially harmful content. We constructed a diagnostically calibrated dialogue repository and validated it through clinician-mediated verification to ensure clinical fidelity.
Human involvement in EDM is limited to data annotation and post-hoc validation, without direct participation in inference or decision-making. Our evaluation adopts a hybrid methodology that combines computational-linguistic measures, bias screening, and a five-dimensional clinical assessment spanning therapeutic alignment, empathetic congruence, contextual consistency, task utility, and safety adherence. Comparative experiments demonstrate marked gains over baseline systems in contextual adaptation, reduction of hazardous outputs, and alignment with clinician judgments.
The main contributions of this paper are threefold. First, we propose a stratified processing paradigm that separates risk assessment from therapeutic response synthesis and embeds layered safety protocols. Second, we present a unified evaluation framework that blends automated metrics with clinical oversight. Third, we establish practical design principles for ethically governed mental-health language systems and indicate future directions in cross-cultural adaptation and multimodal clinical decision support.

TL;DR: We propose a dual-phase prompt framework that enhances safety and therapeutic relevance in mental health language models.