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

Research output: Contribution to journalJournal articleResearchpeer-review

  • Roberto V. Zicari
  • Sheraz Ahmed
  • Julia Amann
  • Stephan Alexander Braun
  • Frédérick Bruneault
  • James Brusseau
  • Erik Campano
  • Megan Coffee
  • Andreas Dengel
  • Alessio Gallucci
  • Thomas Krendl Gilbert
  • Philippe Gottfrois
  • Emmanuel Goffi
  • Thilo Hagendorff
  • Eleanore Hickman
  • Elisabeth Hildt
  • 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
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.
Original languageEnglish
Article number688152
JournalFrontiers in Human Dynamics
Volume3
Number of pages20
ISSN2673-2726
DOIs
Publication statusPublished - Jul 2021

ID: 274276062