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

Michal Klincewicz*, Lily Frank

*Corresponding author for this work

Research output: Contribution to journalConference 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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2681
Publication statusPublished - 2019
Event2nd EXplainable AI in Law Workshop, XAILA 2019 - Madrid, Spain
Duration: 11 Dec 2019 → …

Keywords

  • Ethics
  • Explainability
  • Health care
  • Machine learning

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