TY  - JOUR
A1  - Zicari, Roberto V.
A1  - Brusseau, James
A1  - Blomberg, Stig Nikolaj
A1  - Christensen, Helle Collatz
A1  - Coffee, Megan
A1  - Ganapini, Marianna B.
A1  - Gerke, Sara
A1  - Gilbert, Thomas Krendl
A1  - Hickman, Eleanore
A1  - Hildt, Elisabeth
A1  - Holm, Sune
A1  - Kühne, Ulrich
A1  - Madai, Vince Istvan
A1  - Osika, Walter
A1  - Spezzatti, Andy
A1  - Schnebel, Eberhard
A1  - Tithi, Jesmin Jahan
A1  - Vetter, Dennis
A1  - Westerlund, Magnus
A1  - Wurth, Renee
A1  - Amann, Julia
A1  - Antun, Vegard
A1  - Beretta, Valentina
A1  - Bruneault, Frédérick
A1  - Campano, Erik
A1  - Düdder, Boris
A1  - Gallucci, Alessio
A1  - Goffi, Emmanuel
A1  - Haase, Christoffer Bjerre
A1  - Hagendorff, Thilo
A1  - Kringen, Pedro
A1  - Möslein, Florian
A1  - Ottenheimer, Davi
A1  - Ozols, Matiss
A1  - Palazzani, Laura
A1  - Petrin, Martin
A1  - Tafur, Karin
A1  - Tørresen, Jim
A1  - Volland, Holger
A1  - Kararigas, Georgios
T1  - On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls
T2  - Frontiers in Human Dynamics
N2  - Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.
KW  - artificial intelligence
KW  - cardiac arrest
KW  - case study
KW  - ethical trade-off
KW  - explainable AI
KW  - healthcare
KW  - trust
KW  - trustworthy AI
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/62450
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-624508
SN  - 2673-2726
N1  - SG was supported by a grant from the Collaborative Research Program for Biomedical Innovation Law, a scientifically independent collaborative research program supported by a Novo Nordisk Foundation grant (NNF17SA0027784). JA received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777107 (PRECISE4Q). TH was supported by the Cluster of Excellence “Machine Learning—New Perspectives for Science” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—Reference Number EXC 2064/1—Project ID 390727645. All other authors did not receive any funding (neither private nor public) to conduct this work.
VL  - 3
IS  - art. 673104
SP  - 1
EP  - 24
PB  - Frontiers Media
CY  - Lausanne
ER  -