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Status: Bibliographieeintrag

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Verfasst von:Herrmann, Markus [VerfasserIn]   i
 Wabro, Andreas [VerfasserIn]   i
 Winkler, Eva C. [VerfasserIn]   i
Titel:Percentages and reasons
Titelzusatz:aI explainability and ultimate human responsibility within the medical field
Verf.angabe:Markus Herrmann, Andreas Wabro, Eva Winkler
E-Jahr:2024
Jahr:9 April 2024
Umfang:10 S.
Fussnoten:Gesehen am 09.09.2024
Titel Quelle:Enthalten in: Ethics and information technology
Ort Quelle:Dordrecht [u.a.] : Springer Science + Business Media B.V, 1999
Jahr Quelle:2024
Band/Heft Quelle:26(2024), 2, Artikel-ID 26, Seite 1-10
ISSN Quelle:1572-8439
Abstract:With regard to current debates on the ethical implementation of AI, especially two demands are linked: the call for explainability and for ultimate human responsibility. In the medical field, both are condensed into the role of one person: It is the physician to whom AI output should be explainable and who should thus bear ultimate responsibility for diagnostic or treatment decisions that are based on such AI output. In this article, we argue that a black box AI indeed creates a rationally irresolvable epistemic situation for the physician involved. Specifically, strange errors that are occasionally made by AI sometimes detach its output from human reasoning. Within this article it is further argued that such an epistemic situation is problematic in the context of ultimate human responsibility. Since said strange errors limit the promises of explainability and the concept of explainability frequently appears irrelevant or insignificant when applied to a diverse set of medical applications, we deem it worthwhile to reconsider the call for ultimate human responsibility.
DOI:doi:10.1007/s10676-024-09764-8
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1007/s10676-024-09764-8
 DOI: https://doi.org/10.1007/s10676-024-09764-8
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:AI
 Artificial Intelligence
 Black box
 Explainability
 Interpretability
 Medical Ethics
 Responsibility
K10plus-PPN:1902152204
Verknüpfungen:→ Zeitschrift

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