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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorLoi, Michele-
dc.contributor.authorHeitz, Christoph-
dc.contributor.authorChristen, Markus-
dc.date.accessioned2022-03-04T13:02:12Z-
dc.date.available2022-03-04T13:02:12Z-
dc.date.issued2020-
dc.identifier.isbn978-1-7281-7177-7de_CH
dc.identifier.urihttps://www.zora.uzh.ch/id/eprint/190563/de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24471-
dc.description.abstractUp to date, more than 80 codes exist for handling ethical risks of artificial intelligence and big data. In this paper, we analyse where those codes converge and where they differ. Based on an in-depth analysis of 20 guidelines, we identify three procedural action types (1. control and document, 2. inform, 3. assign responsibility) as well as four clusters of ethical values whose promotion or protection is supported by the procedural activities. We achieve a synthesis of previous approaches with a framework of seven principles, combining the four principles of biomedical ethics with three distinct procedural principles: control, transparency and accountability.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofProceedings of the 7th SDSde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData ethicde_CH
dc.subjectEthical guidelinede_CH
dc.subjectArtificial intelligencede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc170: Ethikde_CH
dc.titleA comparative assessment and synthesis of twenty ethics codes on AI and big datade_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1109/SDS49233.2020.00015de_CH
zhaw.conference.details7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end46de_CH
zhaw.pages.start41de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Loi, M., Heitz, C., & Christen, M. (2020). A comparative assessment and synthesis of twenty ethics codes on AI and big data [Conference paper]. Proceedings of the 7th SDS, 41–46. https://doi.org/10.1109/SDS49233.2020.00015
Loi, M., Heitz, C. and Christen, M. (2020) ‘A comparative assessment and synthesis of twenty ethics codes on AI and big data’, in Proceedings of the 7th SDS. IEEE, pp. 41–46. Available at: https://doi.org/10.1109/SDS49233.2020.00015.
M. Loi, C. Heitz, and M. Christen, “A comparative assessment and synthesis of twenty ethics codes on AI and big data,” in Proceedings of the 7th SDS, 2020, pp. 41–46. doi: 10.1109/SDS49233.2020.00015.
LOI, Michele, Christoph HEITZ und Markus CHRISTEN, 2020. A comparative assessment and synthesis of twenty ethics codes on AI and big data. In: Proceedings of the 7th SDS [online]. Conference paper. IEEE. 2020. S. 41–46. ISBN 978-1-7281-7177-7. Verfügbar unter: https://www.zora.uzh.ch/id/eprint/190563/
Loi, Michele, Christoph Heitz, and Markus Christen. 2020. “A Comparative Assessment and Synthesis of Twenty Ethics Codes on AI and Big Data.” Conference paper. In Proceedings of the 7th SDS, 41–46. IEEE. https://doi.org/10.1109/SDS49233.2020.00015.
Loi, Michele, et al. “A Comparative Assessment and Synthesis of Twenty Ethics Codes on AI and Big Data.” Proceedings of the 7th SDS, IEEE, 2020, pp. 41–46, https://doi.org/10.1109/SDS49233.2020.00015.


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