mlsanityData to 14 Oct 2026

VERA-MH Concept Paper

arXiv 2510.15297 ↗First posted 17 Oct 2025cs.CY cs.AI cs.HCcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 17 Oct 2025.

Builds on

5 of 14 references.

References · 5

  1. 2024Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligencecited by 68
  2. 2025The Leaderboard Illusioncited by 66
  3. 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 60
  4. 2025Medical Large Language Model Benchmarks Should Prioritize Construct Validitycited by 38
  5. 2025Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effectscited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.