Frontiers in Human Dynamics (Jul 2021)

Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

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

DOI
https://doi.org/10.3389/fhumd.2021.688152
Journal volume & issue
Vol. 3

Abstract

Read online

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.

Keywords