大型语言模型在公共卫生领域采用的风险分类与反思工具
A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health
AI总结：
本研究通过焦点小组构建了LLM在公共卫生应用中的风险分类体系，涵盖四个风险维度，并提供反思工具以支持风险反思性决策。
摘要：
大型语言模型（LLM）的最新突破引发了对其在不同领域作为信息来源或沟通工具潜在应用的兴趣与担忧。在公共卫生领域，由于风险高且影响遍及不同人群，采用LLM带来了需要全面评估的独特挑战。然而，评估公共卫生潜在风险的结构化方法仍未得到充分探索。为弥补这一空白，我们与公共卫生专业人员及有亲身经历的个体进行了焦点小组讨论，以揭示他们的关切，这些关切涉及三个需要高质量信息的关键公共卫生问题：传染病预防（疫苗）、慢性与福祉护理（阿片类药物使用障碍）以及社区健康与安全（亲密伴侣暴力）。我们将参与者的观点综合成一个风险分类体系，识别并情境化了当LLM与传统健康传播并列时可能引入的潜在危害。该分类体系强调了四个风险维度：对个体的风险、以人为本的护理、信息生态系统和技术问责。针对每个维度，我们详细阐述了具体风险，并提供了示例反思问题，以帮助从业者采取风险反思的方法。通过总结LLM的独特特征并将其与已识别的风险联系起来，我们讨论了重新审视先前信息行为心智模型的必要性，并通过亲身经历和实际实践，用外部有效性和领域专业知识补充评估。总体而言，这项工作为计算和公共卫生领域的人们提供了一个共享词汇和反思工具，以协作地预测、评估和缓解风险，决定何时（或不）采用LLM能力以及如何减轻伤害。
英文摘要：
Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as information sources or communication tools across different domains. In public health, where stakes are high and impacts extend across diverse populations, adopting LLMs poses unique challenges that require thorough evaluation. However, structured approaches for assessing potential risks in public health remain under-explored. To address this gap, we conducted focus groups with public health professionals and individuals with lived experience to unpack their concerns, situated across three distinct and critical public health issues that demand high-quality information: infectious disease prevention (vaccines), chronic and well-being care (opioid use disorder), and community health and safety (intimate partner violence). We synthesize participants' perspectives into a risk taxonomy, identifying and contextualizing the potential harms LLMs may introduce when positioned alongside traditional health communication. This taxonomy highlights four dimensions of risk to individuals, human-centered care, information ecosystem, and technology accountability. For each dimension, we unpack specific risks and offer example reflection questions to help practitioners adopt a risk-reflexive approach. By summarizing distinctive LLM characteristics and linking them to identified risks, we discuss the need to revisit prior mental models of information behaviors and complement evaluations with external validity and domain expertise through lived experience and real-world practices. Together, this work contributes a shared vocabulary and reflection tool for people in both computing and public health to collaboratively anticipate, evaluate, and mitigate risks in deciding when to employ LLM capabilities (or not) and how to mitigate harm.
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