PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Support

TL;DR

This paper introduces PAIR-SAFE, a paired-agent framework that enhances AI mental health support by providing transparent, runtime auditing and refinement grounded in clinical standards, improving response quality and safety.

Contribution

The paper presents a novel paired-agent system integrating a supervisory Judge grounded in MITI-4 to audit and refine AI responses in mental health support, enhancing transparency and clinical alignment.

Findings

Significant improvements in MITI dimensions like Partnership and Collaboration.

Quantitative evaluation shows enhanced response quality.

Expert qualitative analysis confirms the effectiveness of runtime supervision.

Abstract

Large language models (LLMs) are increasingly used for mental health support, yet they can produce responses that are overly directive, inconsistent, or clinically misaligned, particularly in sensitive or high-risk contexts. Existing approaches to mitigating these risks largely rely on implicit alignment through training or prompting, offering limited transparency and runtime accountability. We introduce PAIR-SAFE, a paired-agent framework for auditing and refining AI-generated mental health support that integrates a Responder agent with a supervisory Judge agent grounded in the clinically validated Motivational Interviewing Treatment Integrity (MITI-4) framework. The Judgeaudits each response and provides structuredALLOW or REVISE decisions that guide runtime response refinement. We simulate counseling interactions using a support-seeker simulator derived from human-annotated motivational interviewing data. We find that Judge-supervised interactions show significant improvements in key MITI dimensions, including Partnership, Seek Collaboration, and overall Relational quality. Our quantitative findings are supported by qualitative expert evaluation, which further highlights the nuances of runtime supervision. Together, our results reveal that such pairedagent approach can provide clinically grounded auditing and refinement for AI-assisted conversational mental health support.