医疗联邦学习隐含伦理风险，催生新型双重黑箱问题。
Federated learning, ethics, and the double black box problem in medical AI
- 提出联邦学习在医疗中产生新型数据与模型双层不透明性
- 揭示医疗联邦学习潜在收益被夸大，存在实际伦理隐患
- 为医疗AI伦理审查提供关键挑战框架，适合政策制定者参考
联邦学习（FL）是一种允许多个设备或机构在不共享本地数据的前提下协同训练模型的机器学习方法，被视为缓解医疗人工智能中患者隐私担忧的有前景方案。然而，医疗联邦学习系统自身的伦理风险迄今未受充分关注。本文旨在填补这一空白，指出医疗联邦学习带来一种新型不透明性——联邦不透明性，进而引发医疗AI中的独特双重黑箱问题。文章揭示若干情形下医疗联邦学习的预期优势可能被夸大，并最终强调必须克服一系列关键挑战，才能使联邦学习在医学领域实现伦理可行性。
原文摘要 · Abstract (English)
Federated learning (FL) is a machine learning approach that allows multiple devices or institutions to collaboratively train a model without sharing their local data with a third-party. FL is considered a promising way to address patient privacy concerns in medical artificial intelligence. The ethical risks of medical FL systems themselves, however, have thus far been underexamined. This paper aims to address this gap. We argue that medical FL presents a new variety of opacity -- federation opacity -- that, in turn, generates a distinctive double black box problem in healthcare AI. We highlight several instances in which the anticipated benefits of medical FL may be exaggerated, and conclude by highlighting key challenges that must be overcome to make FL ethically feasible in medicine.
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