Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy

Michal Klincewicz, Lily Frank

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationXAILA 2019 EXplainable AI in Law 2019: Proceedings of the 2nd EXplainable AI in Law Workshop (XAILA 2019) co-located with 32nd International Conference on Legal Knowledge and Information Systems (JURIX 2019)
EditorsGrzegorz J. Nalepa
PublisherCEUR-WS.org
Number of pages12
Publication statusPublished - 2019
Event2nd EXplainable AI in Law Workshop, XAILA 2019 - Madrid, Spain
Duration: 11 Dec 2019 → …

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS.org
Volume2681
ISSN (Print)1613-0073

Conference

Conference2nd EXplainable AI in Law Workshop, XAILA 2019
CountrySpain
CityMadrid
Period11/12/19 → …

Keywords

  • Ethics
  • Explainability
  • Health care
  • Machine learning

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