为语言模型心理疾病推断研究设计伦理工具，提升临床适用性。
Introducing ELLIPS: An Ethics-Centered Approach to Research on LLM-Based Inference of Psychiatric Conditions
- 提出七项核心伦理原则，构建可操作的伦理评估框架
- 开发 ELLIPS 工具，指导数据、模型与部署决策
- 适合关注医疗AI伦理的研究者和开发者
随着全球心理健康服务体系难以满足需求，利用语言模型从语言表达中推断神经精神障碍或心理病理特征的研究日益增多。然而，当前研究多因忽视关键伦理问题，导致成果临床应用有限。本文系统梳理该领域的伦理图景，提出七项核心伦理原则，并将其转化为可操作的伦理工具 ELLIPS，指导研究人员在数据选择、模型架构、评估及部署等环节做出符合伦理的决策。通过案例研究展示其应用价值，旨在推动具备真实世界应用潜力的心理健康语言模型发展。
原文摘要 · Abstract (English)
As mental health care systems worldwide struggle to meet demand, there is increasing focus on using language models to infer neuropsychiatric conditions or psychopathological traits from language production. Yet, so far, this research has only delivered solutions with limited clinical applicability, due to insufficient consideration of ethical questions crucial to ensuring the synergy between possible applications and model design. To accelerate progress towards clinically applicable models, our paper charts the ethical landscape of research on language-based inference of psychopathology and provides a practical tool for researchers to navigate it. We identify seven core ethical principles that should guide model development and deployment in this domain, translate them into ELLIPS, an ethical toolkit operationalizing these principles into questions that can guide researchers' choices with respect to data selection, architectures, evaluation, and model deployment, and provide a case study exemplifying its use. With this, we aim to facilitate the emergence of model technology with concrete potential for real-world applicability.
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