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

Roberto V. Zicari; Sheraz Ahmed; Julia Amann; Stephan Alexander Braun; John Brodersen; Frédérick Bruneault; James Brusseau; Erik Campano; Megan Coffee; Andreas Dengel; Boris Düdder; Alessio Gallucci; Thomas Krendl Gilbert; Philippe Gottfrois; Emmanuel Goffi; Christoffer Bjerre Haase; Thilo Hagendorff; Eleanore Hickman; Elisabeth Hildt; Sune Holm; 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

doi:10.3389/fhumd.2021.688152

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

Roberto V. Zicari^*
, Sheraz Ahmed
, Julia Amann
, Stephan Alexander Braun
, John Brodersen
, Frédérick Bruneault
, James Brusseau
, Erik Campano
, Megan Coffee
, Andreas Dengel
, Boris Düdder
, Alessio Gallucci
, Thomas Krendl Gilbert
, Philippe Gottfrois
, Emmanuel Goffi
, Christoffer Bjerre Haase
, Thilo Hagendorff
, Eleanore Hickman
, Elisabeth Hildt
, Sune Holm

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

^*Corresponding author for this work

Arcada University of Applied Sciences
German Research Center for Artificial Intelligence
Swiss Federal Institute of Technology Zurich
University of Münster
University of Copenhagen
Primary Health Care Research Unit
Université du Québec à Montréal
Pace University
Umeå University
New York University
Institute for Surface and Thin Film Technologies (IFOS) at the University of Kaiserslautern-Landau (RPTU)
Eindhoven University of Technology
University of California at Berkeley
The Global AI Ethics Institute
Deakin University
University of Tübingen
University of Cambridge
Illinois Institute of Technology
Hautmedizin Bad Soden
Charité Universitätsmedizin Berlin
Birmingham City University
Seinäjoki University of Applied Sciences
AI Ethics
University of Manchester
Wellcome Trust Sanger Institute
Intel Labs
University of Turin
Z-Inspection® Initiative
Harvard University

Research output: Contribution to journal › Article › peer-review

57 Citations (Scopus)

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.

Original language	English
Article number	688152
Journal	Frontiers in Human Dynamics
Volume	3
DOIs	https://doi.org/10.3389/fhumd.2021.688152
Publication status	Published - 2021
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Z-inspection
artificial intelligence
ethical co-design
ethics
healthcare
malignant melanoma
trustworthy AI
trustworthy AI Co-design

Access to Document

10.3389/fhumd.2021.688152

Co-design of a trustworthyLicence: CC BY

Cite this

Zicari, R. V., Ahmed, S., Amann, J., Braun, S. A., Brodersen, J., Bruneault, F., Brusseau, J., Campano, E., Coffee, M., Dengel, A., Düdder, B., Gallucci, A., Gilbert, T. K., Gottfrois, P., Goffi, E., Haase, C. B., Hagendorff, T., Hickman, E., Hildt, E., ... Wurth, R. (2021). Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier. Frontiers in Human Dynamics, 3, Article 688152. https://doi.org/10.3389/fhumd.2021.688152

@article{4387d1250fec4d989ecb18ec72355540,

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

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 = "Z-inspection, artificial intelligence, ethical co-design, ethics, healthcare, malignant melanoma, trustworthy AI, trustworthy AI Co-design",

author = "Zicari, \{Roberto V.\} and Sheraz Ahmed and Julia Amann and Braun, \{Stephan Alexander\} and John Brodersen and Fr{\'e}d{\'e}rick Bruneault and James Brusseau and Erik Campano and Megan Coffee and Andreas Dengel and Boris D{\"u}dder and Alessio Gallucci and Gilbert, \{Thomas Krendl\} and Philippe Gottfrois and Emmanuel Goffi and Haase, \{Christoffer Bjerre\} and Thilo Hagendorff and Eleanore Hickman and Elisabeth Hildt and Sune Holm and Pedro Kringen and Ulrich K{\"u}hne and Adriano Lucieri and Madai, \{Vince I.\} and Moreno-S{\'a}nchez, \{Pedro A.\} and Oriana Medlicott and Matiss Ozols and Eberhard Schnebel and Andy Spezzatti and Tithi, \{Jesmin Jahan\} and Steven Umbrello and Dennis Vetter and Holger Volland and Magnus Westerlund and Renee Wurth",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 Zicari, Ahmed, Amann, Braun, Brodersen, Bruneault, Brusseau, Campano, Coffee, Dengel, D{\"u}dder, Gallucci, Gilbert, Gottfrois, Goffi, Haase, Hagendorff, Hickman, Hildt, Holm, Kringen, K{\"u}hne, Lucieri, Madai, Moreno-S{\'a}nchez, Medlicott, Ozols, Schnebel, Spezzatti, Tithi, Umbrello, Vetter, Volland, Westerlund and Wurth.",

year = "2021",

doi = "10.3389/fhumd.2021.688152",

language = "English",

volume = "3",

journal = "Frontiers in Human Dynamics",

issn = "2673-2726",

}

Zicari, RV, Ahmed, S, Amann, J, Braun, SA, Brodersen, J, Bruneault, F, Brusseau, J, Campano, E, Coffee, M, Dengel, A, Düdder, B, Gallucci, A, Gilbert, TK, Gottfrois, P, Goffi, E, Haase, CB, Hagendorff, T, Hickman, E, Hildt, E, Holm, S, Kringen, P, Kühne, U, Lucieri, A, Madai, VI, Moreno-Sánchez, PA, Medlicott, O, Ozols, M, Schnebel, E, Spezzatti, A, Tithi, JJ, Umbrello, S, Vetter, D, Volland, H, Westerlund, M & Wurth, R 2021, 'Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier', Frontiers in Human Dynamics, vol. 3, 688152. https://doi.org/10.3389/fhumd.2021.688152

TY - JOUR

T1 - Co-Design of a Trustworthy AI System in Healthcare

T2 - Deep Learning Based Skin Lesion Classifier

AU - Zicari, Roberto V.

AU - Ahmed, Sheraz

AU - Amann, Julia

AU - Braun, Stephan Alexander

AU - Brodersen, John

AU - Bruneault, Frédérick

AU - Brusseau, James

AU - Campano, Erik

AU - Coffee, Megan

AU - Dengel, Andreas

AU - Düdder, Boris

AU - Gallucci, Alessio

AU - Gilbert, Thomas Krendl

AU - Gottfrois, Philippe

AU - Goffi, Emmanuel

AU - Haase, Christoffer Bjerre

AU - Hagendorff, Thilo

AU - Hickman, Eleanore

AU - Hildt, Elisabeth

AU - Holm, Sune

AU - Kringen, Pedro

AU - Kühne, Ulrich

AU - Lucieri, Adriano

AU - Madai, Vince I.

AU - Moreno-Sánchez, Pedro A.

AU - Medlicott, Oriana

AU - Ozols, Matiss

AU - Schnebel, Eberhard

AU - Spezzatti, Andy

AU - Tithi, Jesmin Jahan

AU - Umbrello, Steven

AU - Vetter, Dennis

AU - Volland, Holger

AU - Westerlund, Magnus

AU - Wurth, Renee

N1 - Publisher Copyright: Copyright © 2021 Zicari, Ahmed, Amann, Braun, Brodersen, Bruneault, Brusseau, Campano, Coffee, Dengel, Düdder, Gallucci, Gilbert, Gottfrois, Goffi, Haase, Hagendorff, Hickman, Hildt, Holm, Kringen, Kühne, Lucieri, Madai, Moreno-Sánchez, Medlicott, Ozols, Schnebel, Spezzatti, Tithi, Umbrello, Vetter, Volland, Westerlund and Wurth.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Z-inspection

KW - artificial intelligence

KW - ethical co-design

KW - ethics

KW - healthcare

KW - malignant melanoma

KW - trustworthy AI

KW - trustworthy AI Co-design

UR - https://www.scopus.com/pages/publications/85121899656

U2 - 10.3389/fhumd.2021.688152

DO - 10.3389/fhumd.2021.688152

M3 - Article

AN - SCOPUS:85121899656

SN - 2673-2726

VL - 3

JO - Frontiers in Human Dynamics

JF - Frontiers in Human Dynamics

M1 - 688152

ER -

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

Abstract

UN SDGs

Keywords

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