VERA-MH Concept Paper

• URL: http://arxiv.org/abs/2510.15297v2
• Date: Wed, 22 Oct 2025 02:19:06 GMT
• Title: VERA-MH Concept Paper
• Authors: Luca Belli, Kate Bentley, Will Alexander, Emily Ward, Matt Hawrilenko, Kelly Johnston, Mill Brown, Adam Chekroud,
• Abstract summary: We introduce VERA-MH, an automated evaluation of the safety of AI chatbots used in mental health contexts.<n>To fully automate the process, we used two ancillary AI agents.<n>Simulated conversations are then passed to a judge-agent who scores them based on the rubric.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in mental health contexts, with an initial focus on suicide risk. Practicing clinicians and academic experts developed a rubric informed by best practices for suicide risk management for the evaluation. To fully automate the process, we used two ancillary AI agents. A user-agent model simulates users engaging in a mental health-based conversation with the chatbot under evaluation. The user-agent role-plays specific personas with pre-defined risk levels and other features. Simulated conversations are then passed to a judge-agent who scores them based on the rubric. The final evaluation of the chatbot being tested is obtained by aggregating the scoring of each conversation. VERA-MH is actively under development and undergoing rigorous validation by mental health clinicians to ensure user-agents realistically act as patients and that the judge-agent accurately scores the AI chatbot. To date we have conducted preliminary evaluation of GPT-5, Claude Opus and Claude Sonnet using initial versions of the VERA-MH rubric and used the findings for further design development. Next steps will include more robust clinical validation and iteration, as well as refining actionable scoring. We are seeking feedback from the community on both the technical and clinical aspects of our evaluation.

Related papers

• VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health [0.0]
  The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet the urgent need for an evidence-based, automated safety benchmark.<n>This study aimed to examine the clinical validity and reliability of VERA-MH for evaluating AI safety in suicide risk detection and response.
  arXiv  Detail & Related papers  (2026-02-04T22:17:04Z)
• EvoClinician: A Self-Evolving Agent for Multi-Turn Medical Diagnosis via Test-Time Evolutionary Learning [72.70291772077738]
  We propose Med-Inquire, a new benchmark designed to evaluate an agent's ability to perform multi-turn diagnosis.<n>We then introduce EvoClinician, a self-evolving agent that learns efficient diagnostic strategies at test time.<n>Our experiments show EvoClinician outperforms continual learning baselines and other self-evolving agents like memory agents.
  arXiv  Detail & Related papers  (2026-01-30T13:26:18Z)
• LunaAI: A Polite and Fair Healthcare Guidance Chatbot [0.7696728525672148]
  Many existing systems fall short in emotional intelligence, fairness, and politeness, which are essential for building patient trust.<n>This study addresses the challenge of integrating ethical communication principles by designing and evaluating LunaAI, a healthcare prototype.
  arXiv  Detail & Related papers  (2026-01-12T13:44:00Z)
• DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services [49.70819009392778]
  Large Language Models (LLMs) and Multi-Agent Systems (MAS) offer opportunities to augment dispatchers.<n>This study aimed to develop and evaluate a taxonomy-grounded, multi-agent system for simulating realistic scenarios.
  arXiv  Detail & Related papers  (2025-10-24T08:01:21Z)
• Mentalic Net: Development of RAG-based Conversational AI and Evaluation Framework for Mental Health Support [0.0]
  Mentalic Net Conversational AI has a BERT Score of 0.898, with other evaluation metrics falling within satisfactory ranges.<n>We advocate for a human-in-the-loop approach and a long-term, responsible strategy in developing such transformative technologies.
  arXiv  Detail & Related papers  (2025-08-27T03:44:56Z)
• TestAgent: An Adaptive and Intelligent Expert for Human Assessment [62.060118490577366]
  We propose TestAgent, a large language model (LLM)-powered agent designed to enhance adaptive testing through interactive engagement.<n>TestAgent supports personalized question selection, captures test-takers' responses and anomalies, and provides precise outcomes through dynamic, conversational interactions.
  arXiv  Detail & Related papers  (2025-06-03T16:07:54Z)
• EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety [42.052840895090284]
  EmoAgent is a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions.<n>EmoEval simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters.<n>EmoGuard serves as an intermediary, monitoring users' mental status, predicting potential harm, and providing corrective feedback to mitigate risks.
  arXiv  Detail & Related papers  (2025-04-13T18:47:22Z)
• Prompt Engineering a Schizophrenia Chatbot: Utilizing a Multi-Agent Approach for Enhanced Compliance with Prompt Instructions [0.0699049312989311]
  Patients with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. While Large Language Models (LLMs) have the potential to make topical mental health information more accessible and engaging, their black-box nature raises concerns about ethics and safety.
  arXiv  Detail & Related papers  (2024-10-10T09:49:24Z)
• Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools [13.386012271835039]
  We created an evaluation framework with 100 benchmark questions and ideal responses.<n>This framework, validated by mental health experts, was tested on a GPT-3.5-turbo-based chatbots.
  arXiv  Detail & Related papers  (2024-08-03T19:57:49Z)
• Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities [48.922660354417204]
  We propose Automated Dynamic Evaluation (AutoDE) to assess an assistant's API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions.
  arXiv  Detail & Related papers  (2024-03-17T07:34:12Z)
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [57.71597869337909]
  We build a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
  arXiv  Detail & Related papers  (2023-08-14T15:13:04Z)
• LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation [18.98839299694749]
  This work focuses on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient simulation. We collaborate with psychiatrists to identify objectives and iteratively develop the dialogue system to closely align with real-world scenarios. In the evaluation experiments, we recruit real psychiatrists and patients to engage in diagnostic conversations with the chatbots, collecting their ratings for assessment.
  arXiv  Detail & Related papers  (2023-05-23T02:25:01Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.