Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier - 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; et al. (Frontiers in Human Dynamics, 2021)
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.