Our paper ” Co-design of a Trustworthy AI System in Healthcare: Deep Learning based Skin Lesion Classifier.” has been accepted for publication in Frontiers in Human Dynamics

Co-design of a Trustworthy AI System in Healthcare: Deep Learning based Skin Lesion Classifier.

Roberto V. Zicari (1)(2)(3), Sheraz Ahmed (4), Julia Amann (5), Stephan Alexander Braun (6)(7), John Brodersen (8)(9), Frédérick Bruneault (10), James Brusseau (11), Erik Campano (12), Megan Coffee (13), Andreas Dengel (4)(14), Boris Düdder (15), Alessio Gallucci (16), Thomas Krendl Gilbert (17), Philippe Gottfrois (18), Emmanuel Goffi (19), Christoffer Bjerre Haase (20), Thilo Hagendorff (21), Eleanore Hickman (22), Elisabeth Hildt (23), Sune Holm (24), Pedro Kringen (1), Ulrich Kühne (25), Adriano Lucieri (4)(14), Vince I. Madai (26)(27)(28), Pedro A. Moreno-Sánchez (29), Oriana Medlicott (30), Matiss Ozols (31)(32), Eberhard Schnebel (1), Andy Spezzatti (33), Jesmin Jahan Tithi (34), Steven Umbrello (35), Dennis Vetter (1), Holger Volland (36), Magnus Westerlund (2), Renee Wurth (37).

(1) Frankfurt Big Data Lab, Goethe University Frankfurt, Germany
(2) Arcada University of Applied Sciences, Helsinki, Finland
(3) Data Science Graduate School, Seoul National University, South Korea
(4) German Research Center for Artificial Intelligence (DFKI) Kaiserslautern, Germany
(5) Health Ethics and Policy Lab,Swiss Federal Institute of Technology (ETH Zurich), Switzerland
(6) Department of Dermatology, University Clinic Münster, Germany
(7) Dept. of Dermatology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
(8) Section of General Practice and Research Unit for General Practice, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Danemark
(9) Primary Health Care Research Unit, Region Zealand, Denmark
(10) École des médias, Université du Québec à Montréal and Philosophie, Collège André-Laurendeau, Canada
(11) Philosophy Department, Pace University, New York, USA
(12) Department of Informatics, Umeå University, Sweden
(13) Department of Medicine and Division of Infectious Diseases and Immunology, NYU Grossman School of Medicine, New York, USA
(14) Department of Computer Science, TU Kaiserslautern, Germany
(15) Department of Computer Science (DIKU), University of Copenhagen (UCPH), Denmark
(16) Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands.
(17) Center for Human-Compatible AI, University of California, Berkeley, USA
(18) Department of Biomedical Engineering, Basel University, Switzerland
(19) The Global AI Ethics Institute, France
(20) Section for Health Service Research and Section for General Practice, Department of Public Health, University of Copenhagen, Denmark. Centre for Research in Assessment and Digital Learning, Deakin University, Melbourne, Australia (21) Ethics & Philosophy Lab, University of Tuebingen , Germany
(22) Faculty of Law, University of Cambridge, UK
(23) Center for the Study of Ethics in the Professions, Illinois Institute of Technology Chicago, USA
(24) Department of Food and Resource Economics, Faculty of Science, University of Copenhagen, DK
(25) “Hautmedizin Bad Soden”, Germany
(26) Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Germany
(27) QUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Germany
(28) School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, United Kingdom
(29) School of Healthcare and Social Work, Seinäjoki University of Applied Sciences (SeAMK), Finland
(30) Freelance researcher, writer and consultant in AI Ethics, UK
(31) Division of Cell Matrix Biology and Regenerative Medicine, The University of Manchester, UK
(32) Human Genetics, Wellcome Sanger Institute, UK
(33) Industrial Engineering & Operation Research, UC Berkeley, USA
(34) Intel Labs, Santa Clara, CA, USA
(35) Institute for Ethics and Emerging Technologies, University of Turin, Italy

(36) Z-Inspection® Initiative
(37) T.H Chan School of Public Health, Harvard University, USA

* Correspondence:

Corresponding Author Roberto V. Zicari

Z-inspection® is a registered trademark

Accepted on 09 June 2021
Front. Hum. Dyn. doi: 10.3389/fhumd.2021.688152

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.

Keywords: Artificial Intelligence, Healthcare, Explainable AI, Trust, Case-Studies, Trustworthy AI, Ethics, Malignant Melanoma, Z-inspection®, Ethical co-design.