Explainability matters: The effect of liability rules on the healthcare sector
This addresses legal and ethical challenges in healthcare AI adoption, but it is incremental as it builds on existing discussions about explainability and liability.
The paper examines how explainability in AI systems affects liability allocation in healthcare, arguing that explainability is crucial for establishing a legal responsibility framework and mitigating defensive medicine risks.
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.