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...
| Publicado en: | Bioethics Vol. 36; no. 2; pp. 143 - 154 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=154715837&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 154715837 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 02699702 6PJ jtl: Bioethics issn: 02699702 maglogo: Y pubinfo: dt: Feb2022 vid: 36 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 154715837 10.1111/bioe.12918 ppf: 143 ppct: 11 formats: tig: atl: Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary? aug: au: Ursin, Frank Timmermann, Cristian Steger, Florian affil: Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Ulm, Germany su: Artificial intelligence Medical ethics Empirical research Bioethics Machine learning Medical protocols Conceptual structures Hospital radiological services Decision making in clinical medicine Algorithms sug: subj: Artificial intelligence Medical ethics Empirical research Bioethics Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) Diagnostic Imaging Centers Machine learning Medical protocols Conceptual structures Hospital radiological services Decision making in clinical medicine Algorithms keyword: black box explainability machine learning medical ethics principlism transparency black box explainability machine learning medical ethics principlism transparency ab: 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? pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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