EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI

2025-09-15

AI Summary

Existing ethical and clinical decision-making benchmarks inadequately assess LLMs’ capacity to navigate intertwined ethical dilemmas—such as confidentiality, autonomy, and fairness—in mental health contexts. To address this gap, we introduce EthicsMH, the first fine-grained, mental health–specific ethical reasoning evaluation framework, accompanied by an open-source benchmark comprising 125 real-world ethically conflicting scenarios. Leveraging model-assisted generation augmented by multi-round expert validation, our methodology integrates moral psychology and clinical practice knowledge to design a structured annotation schema that supports multidimensional evaluation of AI decision justification, explanation quality, and alignment with professional standards. Key contributions include: (i) incorporation of multi-stakeholder perspectives; (ii) expert-aligned reasoning pathways; and (iii) standardized, clinically grounded decision options—collectively establishing a scalable, reproducible evaluation standard for responsible alignment of sensitive healthcare AI and fostering community-driven ethical assessment infrastructure.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society's most delicate decisions.

Problem

• Evaluating AI ethical reasoning in mental health scenarios
• Addressing ethical dilemmas like confidentiality and bias
• Assessing decision accuracy and explanation quality

Innovation

• EthicsMH dataset with structured scenarios
• Evaluates AI ethical reasoning and explanations
• Bridges AI ethics with mental health decisions