Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary?

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term...

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Vydané v:Bioethics Ročník 36; číslo 2; s. 143 - 153
Hlavní autori: Ursin, Frank, Timmermann, Cristian, Steger, Florian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Blackwell Publishing Ltd 01.02.2022
Predmet:
Artificial Intelligence
Bioethics
black box
Clinical decision making
Concepts
Decision making
Ethical Analysis
Ethics
explainability
Health services
Humans
Intelligence
Learning algorithms
Machine learning
Medical decision making
medical ethics
Morals
Principles
principlism
Questions
Radiology
transparency
principlism
black box
explainability
machine learning
medical ethics
transparency
ISSN:0269-9702, 1467-8519, 1467-8519
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Abstract Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black‐box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision‐making entails conceptual questions: Is explicability a free‐standing principle? Is it already covered by the well‐established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual‐ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?
AbstractList Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black‐box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision‐making entails conceptual questions: Is explicability a free‐standing principle? Is it already covered by the well‐established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual‐ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?
Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?
Author Ursin, Frank
Timmermann, Cristian
Steger, Florian
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Keywords principlism
black box
explainability
machine learning
medical ethics
transparency
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SubjectTerms Artificial Intelligence
Bioethics
black box
Clinical decision making
Concepts
Decision making
Ethical Analysis
Ethics
explainability
Health services
Humans
Intelligence
Learning algorithms
Machine learning
Medical decision making
medical ethics
Morals
Principles
principlism
Questions
Radiology
transparency
Title Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary?
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