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dc.contributor.author
Zicari, Roberto V.
dc.contributor.author
Amann, Julia
dc.contributor.author
Bruneault, Frédérick
dc.contributor.author
Coffee, Megan
dc.contributor.author
Düdder, Boris
dc.contributor.author
Hickman, Eleanore
dc.contributor.author
Gallucci, Alessio
dc.contributor.author
Gilbert, Thomas Krendl
dc.contributor.author
Hagendorff, Thilo
dc.contributor.author
van Halem, Irmhild
dc.contributor.author
Hildt, Elisabeth
dc.contributor.author
Kararigas, Georgios
dc.contributor.author
Kringen, Pedro
dc.contributor.author
Madai, Vince I.
dc.contributor.author
Mathez, Emilie Wiinblad
dc.contributor.author
Tithi, Jesmin J.
dc.contributor.author
Vetter, Dennis
dc.contributor.author
Westerlund, Magnus
dc.contributor.author
Wurth, Renee
dc.date.accessioned
2024-10-16T13:22:14Z
dc.date.available
2022-06-23T11:22:37Z
dc.date.available
2022-06-24T08:04:18Z
dc.date.available
2024-10-16T13:22:14Z
dc.date.issued
2022-06-20
dc.identifier.other
10.48550/arXiv.2206.09887
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/554283
dc.identifier.doi
10.3929/ethz-b-000554283
dc.description.abstract
This report is a methodological reflection on Z-Inspection. Z-Inspection is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Cornell University
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subject
Computers and Society (cs.CY)
en_US
dc.subject
FOS: Computer and information sciences
en_US
dc.title
How to Assess Trustworthy AI in Practice
en_US
dc.type
Working Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
ethz.journal.title
arXiv
ethz.pages.start
2206.09887
en_US
ethz.size
51 p.
en_US
ethz.version.edition
v1
en_US
ethz.identifier.arxiv
2206.09887
ethz.publication.place
Ithaca, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02540 - Institut für Translationale Medizin / Institute of Translational Medicine::09614 - Vayena, Eftychia / Vayena, Eftychia
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02070 - Dep. Gesundheitswiss. und Technologie / Dep. of Health Sciences and Technology::02540 - Institut für Translationale Medizin / Institute of Translational Medicine::09614 - Vayena, Eftychia / Vayena, Eftychia
en_US
ethz.date.deposited
2022-06-23T11:22:42Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-06-24T08:04:24Z
ethz.rosetta.lastUpdated
2023-02-07T03:45:51Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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