AUTHOR=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 TITLE=Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier JOURNAL=Frontiers in Human Dynamics VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2021.688152 DOI=10.3389/fhumd.2021.688152 ISSN=2673-2726 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.