TY - JOUR
T1 - Co-Design of a Trustworthy AI System in Healthcare
T2 - Deep Learning Based Skin Lesion Classifier
AU - Zicari, Roberto V.
AU - Ahmed, Sheraz
AU - Amann, Julia
AU - Braun, Stephan Alexander
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
N1 - Publisher Copyright:
Copyright © 2021 Zicari, Ahmed, Amann, Braun, Brodersen, Bruneault, Brusseau, Campano, Coffee, Dengel, Düdder, Gallucci, Gilbert, Gottfrois, Goffi, Haase, Hagendorff, Hickman, Hildt, Holm, Kringen, Kühne, Lucieri, Madai, Moreno-Sánchez, Medlicott, Ozols, Schnebel, Spezzatti, Tithi, Umbrello, Vetter, Volland, Westerlund and Wurth.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Z-inspection
KW - artificial intelligence
KW - ethical co-design
KW - ethics
KW - healthcare
KW - malignant melanoma
KW - trustworthy AI
KW - trustworthy AI Co-design
UR - https://www.scopus.com/pages/publications/85121899656
U2 - 10.3389/fhumd.2021.688152
DO - 10.3389/fhumd.2021.688152
M3 - Article
AN - SCOPUS:85121899656
SN - 2673-2726
VL - 3
JO - Frontiers in Human Dynamics
JF - Frontiers in Human Dynamics
M1 - 688152
ER -
