分析临床大模型偏见，提升医疗AI公平性与可信度
Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency
- 构建医疗通用语料库HC4，超890亿词元
- 发现不同人群在阿片类药物处方上存在显著差异
- 提出医疗专属评估方法，助力可信临床AI
大型语言模型在医疗领域具有变革潜力，但其负责任且公平的发展依赖于对训练数据特征如何影响模型行为的深入理解，包括潜在偏见。当前数据集构建与偏见评估实践普遍缺乏透明度，亟需全面评估框架以增强信任并指导改进。本研究深入分析了临床语言模型可能产生的下游偏见，重点关注不同人口群体（如种族、性别、年龄）在阿片类药物处方上的差异倾向。为此，我们引入了HC4： Healthcare Comprehensive Commons Corpus，一个超过890亿词元的全新、大规模精炼预训练数据集。评估结合现有通用基准与新型医疗专用方法，为临床AI应用中的公平性与安全性提供了关键洞见。
原文摘要 · Abstract (English)
Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and bias assessment often lack the necessary transparency, creating an urgent need for comprehensive evaluation frameworks to foster trust and guide improvements. In this study, we present an in-depth analysis of potential downstream biases in clinical language models, with a focus on differential opioid prescription tendencies across diverse demographic groups, such as ethnicity, gender, and age. As part of this investigation, we introduce HC4: Healthcare Comprehensive Commons Corpus, a novel and extensively curated pretraining dataset exceeding 89 billion tokens. Our evaluation leverages both established general benchmarks and a novel, healthcare-specific methodology, offering crucial insights to support fairness and safety in clinical AI applications.
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