Technological Opacity of Machine Learning in Healthcare

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dc.contributor.authorHerzog, Christian
dc.date.accessioned2023-08-29T11:51:04Z
dc.date.available2023-08-29T11:51:04Z
dc.date.issued2019
dc.description.abstractRecently, a host of propositions for guidelines for the ethical development and use of artificial intelligence (AI) has been published. This body of work contains timely contributions for sensitizing developers to the ethical and societal implications of their work. However, a sustained embedding of ethics in largely algorithm-based technology development, research and studies requires a precise framing of the origins of the new vulnerabilities created. Recently, scholars have been referring to ethics associated with technology that is in some way “opaque” to at least part of its associated stakeholders. This “opacity” can take several forms which will be discussed in this paper. There are various ways in which such an opacity can create vulnerabilities and, hence, relevant ethical, societal, epistemic and regulatory challenges. This paper provides a non-exhaustive list of examples in healthcare that call for educational resources and consideration in development processes that try to reveal and counter these opacities.en
dc.description.sponsorshipThis work has been funded by the Federal Ministry of Education and Research of Germany (BMBF) (grant no.: 16DII111, 16DII112, 16DII113, 16DII114, 16DII115, 16DII116, 16DII117 – „Deutsches Internet-Institut“)
dc.identifier.citationHerzog, C. (2019). Technological Opacity of Machine Learning in Healthcare. Proceedings of the Weizenbaum Conference 2019: Challenges of Digital Inequality, 45–53. https://doi.org/10.34669/WI.CP/2.7
dc.identifier.doihttps://doi.org/10.34669/wi.cp/2.7
dc.identifier.eissn2510-7666
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/49
dc.language.isoeng
dc.publisherWeizenbaum Institute
dc.relation.ispartofhttps://doi.org/10.34669/WI.CP/2.32
dc.relation.ispartofseriesWeizenbaum Conference Proceedings
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectTechnology (Applied sciences)en
dc.subjectTechnology Assessmenten
dc.subjecteffects of technologyen
dc.subjectautomationen
dc.subjectethicsen
dc.subjecthealth care delivery systemen
dc.subjectartificial intelligenceen
dc.subjectTechnik, Technologiede
dc.subjectMachine Learningde
dc.subjectEthical and Societal Implicationsde
dc.subjectTechnological Opacityde
dc.subjectTechnikfolgenabschätzungde
dc.subjectAutomatisierungde
dc.subjectkünstliche Intelligenzde
dc.subjectGesundheitswesende
dc.subjectEthikde
dc.subjectTechnikfolgende
dc.subject.ddc600 Technik
dc.subject.ddc300 Sozialwissenschaften
dc.subject.ddc004 Informatik
dc.titleTechnological Opacity of Machine Learning in Healthcare
dc.typeConferencePaper
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.booktitleProceedings of the Weizenbaum Conference 2019
dcterms.bibliographicCitation.originalpublisherplaceBerlin
dcterms.bibliographicCitation.pageend53
dcterms.bibliographicCitation.pagestart45
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Proceedings of the Weizenbaum Conference 2019: Challenges of Digital Inequality