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

Authors :
Zicari, Roberto V.
Ahmed, Sheraz
Amann, Julia
Braun, Stephan Alexander
Brodersen, John
Bruneault, Frédérick
Brusseau, James
Campano, Erik
Coffee, Megan
Dengel, Andreas
Düdder, Boris
Gallucci, Alessio
Gilbert, Thomas Krendl
Gottfrois, Philippe
Goffi, Emmanuel
Haase, Christoffer Bjerre
Hagendorff, Thilo
Hickman, Eleanore
Hildt, Elisabeth
Holm, Sune
Kringen, Pedro
Kühne, Ulrich
Lucieri, Adriano
Madai, Vince I.
Moreno-Sánchez, Pedro A.
Medlicott, Oriana
Ozols, Matiss
Schnebel, Eberhard
Spezzatti, Andy
Tithi, Jesmin Jahan
Umbrello, Steven
Vetter, Dennis
Volland, Holger
Westerlund, Magnus
Wurth, Renee
Zicari, Roberto V.
Ahmed, Sheraz
Amann, Julia
Braun, Stephan Alexander
Brodersen, John
Bruneault, Frédérick
Brusseau, James
Campano, Erik
Coffee, Megan
Dengel, Andreas
Düdder, Boris
Gallucci, Alessio
Gilbert, Thomas Krendl
Gottfrois, Philippe
Goffi, Emmanuel
Haase, Christoffer Bjerre
Hagendorff, Thilo
Hickman, Eleanore
Hildt, Elisabeth
Holm, Sune
Kringen, Pedro
Kühne, Ulrich
Lucieri, Adriano
Madai, Vince I.
Moreno-Sánchez, Pedro A.
Medlicott, Oriana
Ozols, Matiss
Schnebel, Eberhard
Spezzatti, Andy
Tithi, Jesmin Jahan
Umbrello, Steven
Vetter, Dennis
Volland, Holger
Westerlund, Magnus
Wurth, Renee
Publication Year :
2021

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.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1422053711
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.3389.fhumd.2021.688152