通用企业责任理论在临床人工智能系统中的应用

IF 0.5 4区 社会学 Q3 LAW American Journal of Law & Medicine Pub Date : 2021-12-01 DOI:10.1017/amj.2022.1
Benny Chan
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引用次数: 2

摘要

人工智能的出现在改善临床诊断方面具有巨大潜力。与此同时,还存在对使用这项技术造成的损害承担责任的重要问题。由于人工智能系统的复杂性、不透明性和缺乏可预见性,传统的责任框架不容易适应人工智能系统。在医疗保健领域,这一困难更加严重,因为医生和医疗保健组织等不同行为者在使用医疗技术方面负有不同但相互关联的法律义务。如果没有原则性的方式在这些参与者之间分配责任,患者可能会发现很难从伤病中恢复过来。在这篇文章中,我建议将医生、临床人工智能系统制造商和医院视为共同的企业。这一拟议框架有助于在单一的法律理论下促进不同行为者之间的责任分配。这种方法回应了人们对临床人工智能技术造成的责任差距的担忧,因为它从由疏忽和产品责任体现的个人主义责任观转向了更分散的概念。除了有利于原告的追偿外,一种常见的企业严格责任方法将为相关行为体提供强有力的激励,促使其谨慎行事。
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Applying a Common Enterprise Theory of Liability to Clinical AI Systems
The advent of artificial intelligence (“AI”) holds great potential to improve clinical diagnostics. At the same time, there are important questions of liability for harms arising from the use of this technology. Due to their complexity, opacity, and lack of foreseeability, AI systems are not easily accommodated by traditional liability frameworks. This difficulty is compounded in the health care space where various actors, namely physicians and health care organizations, are subject to distinct but interrelated legal duties regarding the use of health technology. Without a principled way to apportion responsibility among these actors, patients may find it difficult to recover for injuries. In this Article, I propose that physicians, manufacturers of clinical AI systems, and hospitals be considered a common enterprise for the purposes of liability. This proposed framework helps facilitate the apportioning of responsibility among disparate actors under a single legal theory. Such an approach responds to concerns about the responsibility gap engendered by clinical AI technology as it shifts away from individualistic notions of responsibility, embodied by negligence and products liability, toward a more distributed conception. In addition to favoring plaintiff recovery, a common enterprise strict liability approach would create strong incentives for the relevant actors to take care.
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来源期刊
CiteScore
0.80
自引率
16.70%
发文量
8
期刊介绍: desde Enero 2004 Último Numero: Octubre 2008 AJLM will solicit blind comments from expert peer reviewers, including faculty members of our editorial board, as well as from other preeminent health law and public policy academics and professionals from across the country and around the world.
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