解释性影响医疗AI责任划分，决定医生与厂商的法律责任边界。
Explainability matters: The effect of liability rules on the healthcare sector
- 对比无解释性（Oracle）与有解释性（AI同事）两种AI系统
- 解释性直接决定医疗事故中医生与厂商的责任归属
- 适合关注AI法律风险与医疗合规的从业者
可解释性指人工智能系统（AIS）以人类可理解的方式呈现其决策结果的能力，在医疗等关键领域被视为必要。本文探讨两种极端情形：无解释性的‘Oracle’与具备解释性的‘AI同事’。分析表明，AIS的自动化程度与可解释性会影响医疗从业者/机构与AIS制造商之间的责任认定。从法律角度出发，可解释性在构建医疗责任框架中起关键作用，能引导各方行为，降低防御性医疗实践的风险。
原文摘要 · Abstract (English)
Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, ``Oracle'' (without explainability) versus ``AI Colleague'' (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices.
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