Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy
Year: 2020
Type: article
Abstract: This paper provides an analysis of the way in which two foundational principles of medical ethics-the trusted doctor and patient autonomy-can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as to what needs to be done to achieve an acceptable level of explainability in an ML algorithm when it is used in a healthcare context.
Authors: Michał Klincewicz, Lily Frank
Institution: Eindhoven University of Technology
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3089262796