Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However,...

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Veröffentlicht in:	AI & society Jg. 38; H. 2; S. 549 - 563
Hauptverfasser:	Giovanola, Benedetta, Tiribelli, Simona
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2023 Springer Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Bias Computer Science Control Data mining Engineering Economics Ethics Health care Learning algorithms Logistics Machine learning Marketing Mechatronics Medical ethics Methodology of the Social Sciences Organization Original Original Article Performing Arts Philosophy Principles Robotics Discrimination Ethics of algorithms Healthcare machine-learning algorithms Bias Fairness Respect
ISSN:	0951-5666, 1435-5655
Online-Zugang:	Volltext
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