Hoche Marine, Mineeva Olga, Rätsch Gunnar, Vayena Effy, Blasimme Alessandro
Department of Computer Science. Biomedical Informatics Group, ETH Zurich, Zurich, Switzerland.
AI Center, ETH Zurich, Zurich, Switzerland.
PLOS Digit Health. 2025 Mar 18;4(3):e0000728. doi: 10.1371/journal.pdig.0000728. eCollection 2025 Mar.
Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning in clinical workflow have attracted considerable attention including due to complex ethical implications-algorithmic bias being among the most frequently discussed ML models. Here we introduce and discuss a practical ethics framework inductively-generated via normative analysis of the practical challenges in developing an actual clinical ML model (see case study). The framework is usable to identify, measure and address bias in clinical machine learning models, thus improving fairness as to both model performance and health outcomes. We detail a proportionate approach to ML bias by defining the demands of fair ML in light of what is ethically justifiable and, at the same time, technically feasible in light of inevitable trade-offs. Our framework enables ethically robust and transparent decision-making both in the design and the context-dependent aspects of ML bias mitigation, thus improving accountability for both developers and clinical users.
机器学习（ML）可以通过简化决策过程、减少错误、提高临床准确性并确保更好的患者预后，为医学做出巨大贡献。机器学习在临床工作流程中广泛且迅速整合的前景已引起相当大的关注，这其中包括由于复杂的伦理影响——算法偏差是最常被讨论的机器学习模型之一。在此，我们介绍并讨论一个通过对开发实际临床机器学习模型中的实际挑战进行规范分析而归纳生成的实用伦理框架（见案例研究）。该框架可用于识别、衡量和解决临床机器学习模型中的偏差，从而在模型性能和健康结果方面提高公平性。我们通过根据伦理上合理的要求以及鉴于不可避免的权衡在技术上可行的要求来定义公平机器学习的需求，详细阐述了一种针对机器学习偏差的相称方法。我们的框架能够在减轻机器学习偏差的设计和与上下文相关的方面实现符合伦理的稳健且透明的决策，从而提高开发者和临床用户的问责制。
