构建首个心理健康AI伦理推理基准，评估模型在敏感场景中的决策能力。
EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI
- 设计125个临床伦理场景，包含多选项与专家对齐推理。
- 支持评估决策准确率、解释质量及专业规范符合度。
- 适合研究AI伦理、心理健康应用的开发者与学者使用。
大型语言模型在心理健康等敏感领域的应用引发关于伦理推理、公平性与责任对齐的紧迫问题。现有道德与临床决策基准未能充分涵盖心理健康实践中保密性、自主性、利他性与偏见交织的独特困境。为此，我们提出心理健康伦理推理（EthicsMH）基准，包含125个情景，用于评估AI系统在治疗与精神科情境中应对伦理挑战的能力。每个情景配备结构化字段，包括多个决策选项、专家对齐推理、预期模型行为、现实影响及多方利益相关者视角，支持对决策准确性、解释质量与专业规范契合度的综合评估。尽管规模有限且采用模型辅助生成，EthicsMH仍建立了一个连接AI伦理与心理健康决策的任务框架。通过发布该数据集，我们希望提供一个可扩展的种子资源，推动社区与专家共同完善，助力开发能负责任处理社会最敏感议题的AI系统。
原文摘要 · Abstract (English)
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.
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