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=Volume 3 - 2021
  
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=The main contribution of this paper is to show the use of an ethically aligned co-design methodology to ensure a trustworthiness early design of an artificial intelligence (AI) system component for healthcare. The system is aimed to explain the decisions made by deep learning networks when used to analyze images of skin lesions. For that, we use a holistic process, called Z-inspection®, which requires a multidisciplinary team of experts working together with the AI designers and their managers to explore and investigate possible ethical, legal and technical issues that could arise from the future use of the AI system. Our research work is addressing the need for the co-design of trustworthy AI using a holistic approach, rather then using static ethical checklists. This paper is a first reflection of what we learned by co-designing in early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.