Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier

Roberto V.  Zicari; Sheraz Ahmed; Julia Amann; Stephan Alexander Braun; John Brodersen; Frédérick  Bruneault; James Brusseau; Erik Campano; Megan Coffee; Andreas Dengel; Boris Düdder; Alessio Gallucci; Thomas Krendl  Gilbert; Philippe  Gottfrois; Emmanuel Goffi; Christoffer Bjerre Haase; Thilo Hagendorff; Eleanore  Hickman; Elisabeth Hildt; Sune  Holm; Pedro Kringen; Ulrich Kühne; Adriano Lucieri; Vince I.  Madai; Pedro A. Moreno-Sánchez; Oriana Medlicott; Matiss Ozols; Eberhard Schnebel; Andy Spezzatti; Jesmin Jahan  Tithi; Steven Umbrello; Dennis Vetter; Holger Volland; Magnus Westerlund; Renee Wurth

doi:10.3389/fhumd.2021.688152

Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier

Roberto V. Zicari, Sheraz Ahmed, Julia Amann, Stephan Alexander Braun, John Brodersen, Frédérick Bruneault, James Brusseau, Erik Campano, Megan Coffee, Andreas Dengel, Boris Düdder, Alessio Gallucci, Thomas Krendl Gilbert, Philippe Gottfrois, Emmanuel Goffi, Christoffer Bjerre Haase, Thilo Hagendorff, Eleanore Hickman, Elisabeth Hildt, Sune HolmPedro Kringen, Ulrich Kühne, Adriano Lucieri, Vince I. Madai, Pedro A. Moreno-Sánchez, Oriana Medlicott, Matiss Ozols, Eberhard Schnebel, Andy Spezzatti, Jesmin Jahan Tithi, Steven Umbrello, Dennis Vetter, Holger Volland, Magnus Westerlund, Renee Wurth

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

37 Citationer (Scopus)

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Abstract

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

Originalsprog	Engelsk
Artikelnummer	688152
Tidsskrift	Frontiers in Human Dynamics
Vol/bind	3
Antal sider	20
ISSN	2673-2726
DOI	https://doi.org/10.3389/fhumd.2021.688152
Status	Udgivet - jul. 2021

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10.3389/fhumd.2021.688152Licens: CC BY

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion ClassifierForlagets udgivne version, 1,04 MBLicens: CC BY

Laboratory for Trustworthy AI (Ekstern organisation)
Düdder, B. (Medlem)
mar. 2022 → …
Aktivitet: Medlemskab - typer › Medlemskab af forskningsnetværk

Citationsformater

Zicari, R. V., Ahmed, S., Amann, J., Braun, S. A., Brodersen, J., Bruneault, F., Brusseau, J., Campano, E., Coffee, M., Dengel, A., Düdder, B., Gallucci, A., Gilbert, T. K., Gottfrois, P., Goffi, E., Haase, C. B., Hagendorff, T., Hickman, E., Hildt, E., ... Wurth, R. (2021). Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier. Frontiers in Human Dynamics , 3, Artikel 688152. https://doi.org/10.3389/fhumd.2021.688152

Zicari, RV, Ahmed, S, Amann, J, Braun, SA, Brodersen, J, Bruneault, F, Brusseau, J, Campano, E, Coffee, M, Dengel, A, Düdder, B, Gallucci, A, Gilbert, TK, Gottfrois, P, Goffi, E, Haase, CB, Hagendorff, T, Hickman, E, Hildt, E, Holm, S, Kringen, P, Kühne, U, Lucieri, A, Madai, VI, Moreno-Sánchez, PA, Medlicott, O, Ozols, M, Schnebel, E, Spezzatti, A, Tithi, JJ, Umbrello, S, Vetter, D, Volland, H, Westerlund, M & Wurth, R 2021, 'Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier', Frontiers in Human Dynamics , bind 3, 688152. https://doi.org/10.3389/fhumd.2021.688152

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abstract = "This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.",

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AU - Brodersen, John

AU - Bruneault, Frédérick

AU - Brusseau, James

AU - Campano, Erik

AU - Coffee, Megan

AU - Dengel, Andreas

AU - Düdder, Boris

AU - Gallucci, Alessio

AU - Gilbert, Thomas Krendl

AU - Gottfrois, Philippe

AU - Goffi, Emmanuel

AU - Haase, Christoffer Bjerre

AU - Hagendorff, Thilo

AU - Hickman, Eleanore

AU - Hildt, Elisabeth

AU - Holm, Sune

AU - Kringen, Pedro

AU - Kühne, Ulrich

AU - Lucieri, Adriano

AU - Madai, Vince I.

AU - Moreno-Sánchez, Pedro A.

AU - Medlicott, Oriana

AU - Ozols, Matiss

AU - Schnebel, Eberhard

AU - Spezzatti, Andy

AU - Tithi, Jesmin Jahan

AU - Umbrello, Steven

AU - Vetter, Dennis

AU - Volland, Holger

AU - Westerlund, Magnus

AU - Wurth, Renee

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Co-design of a trustworthy AI system in healthcare: Deep learning based skin lesion classifier

Abstract

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Aktiviteter

Laboratory for Trustworthy AI (Ekstern organisation)

Citationsformater