EthicsMH：心理健康人工智能中伦理推理的试点基准
EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI
AI总结：
本文提出心理健康伦理推理试点基准EthicsMH，包含125个场景，用于评估AI系统在心理健康语境下的伦理决策与解释质量，填补了该领域独特伦理困境评估的空白。
摘要：
将大型语言模型（LLM）部署于心理健康及其他敏感领域，引发了关于伦理推理、公平性和负责任对齐的紧迫问题。然而，现有的道德与临床决策基准未能充分捕捉心理健康实践中遇到的独特伦理困境，在这些实践中，保密性、自主性、有益性和偏见经常交织在一起。为了填补这一空白，我们引入了心理健康伦理推理（EthicsMH），这是一个包含125个场景的试点数据集，旨在评估AI系统如何在治疗和精神病学语境中应对充满伦理色彩的状况。每个场景都丰富了结构化字段，包括多个决策选项、专家对齐的推理、预期模型行为、现实世界影响以及多利益相关者观点。这种结构不仅能评估决策准确性，还能评估解释质量以及与专业规范的对齐情况。尽管规模不大且通过模型辅助生成开发，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，我们旨在提供一种可通过社区和专家贡献进行扩展的种子资源，促进能够负责任地处理社会中最微妙决策的AI系统的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
