标题： 当前诉讼对医疗交付人工智能系统设计的影响 显示英文标题

标题： Implications of Current Litigation on the Design of AI Systems for Healthcare Delivery

摘要： 许多关于医疗领域可解释人工智能（XAI）系统的呼吁与希望人工智能问责有关——对人工智能系统造成的危害进行说明、缓解并最终防止这些危害。 由于XAI系统能够为它们的输出提供人类可理解的解释，因此它们通常被视为防止患者受到伤害的主要途径。 然而，当造成伤害时，法律、政策和法规也通过影响受害个体如何获得救济来塑造人工智能的问责。 当前的XAI方法探索了医生在医疗和人际关系方面的需求，以应对患者受到的伤害，但需要理解XAI系统应如何考虑受影响者的法律考量。 我们分析了31个法律案例和报告的伤害，以确定人工智能系统如何影响患者护理的模式。 我们的研究结果反映出患者的医疗护理依赖于一个复杂的利益相关者网络——包括医生、州卫生部门、健康保险公司、护理设施等，而他们在其医疗交付中部署的许多人工智能系统都会对他们的护理产生负面影响。 作为回应，患者别无选择，只能寻求法律救济。 我们通过描述律师和技术人员需要认识到并解决人工智能伤害发生的地方，将焦点从以医生为中心的问责方法转向以患者为中心的问责方法。 我们提出了防止或应对伤害的路径：(1) 通过改变责任结构，以反映多个利益相关者在塑造人工智能系统如何影响患者护理中的作用；以及(2) 通过设计XAI系统，帮助倡导者，如法律代表，他们提供关键的法律专业知识和实际支持，以帮助患者获得救济。 显示英文摘要

摘要： Many calls for explainable AI (XAI) systems in medicine are tied to a desire for AI accountability--accounting for, mitigating, and ultimately preventing harms from AI systems. Because XAI systems provide human-understandable explanations for their output, they are often viewed as a primary path to prevent harms to patients. However, when harm occurs, laws, policies, and regulations also shape AI accountability by impacting how harmed individuals can obtain recourse. Current approaches to XAI explore physicians' medical and relational needs to counter harms to patients, but there is a need to understand how XAI systems should account for the legal considerations of those impacted. We conduct an analysis of 31 legal cases and reported harms to identify patterns around how AI systems impact patient care. Our findings reflect how patients' medical care relies on a complex web of stakeholders--physicians, state health departments, health insurers, care facilities, among others--and many AI systems deployed across their healthcare delivery negatively impact their care. In response, patients have had no option but to seek legal recourse for harms. We shift the frame from physician-centered to patient-centered accountability approaches by describing how lawyers and technologists need to recognize and address where AI harms happen. We present paths for preventing or countering harm (1) by changing liability structures to reflect the role of many stakeholders in shaping how AI systems impact patient care; and (2) by designing XAI systems that can help advocates, such as legal representatives, who provide critical legal expertise and practically support recourse for patients.