Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy
3
Zitationen
2
Autoren
2019
Jahr
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.436 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.311 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.753 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.523 Zit.