TY - JOUR
T1 - On assessing trustworthy AI in healthcare
T2 - Best practice for machine learning as a supportive tool to recognize cardiac arrest in emergency calls
AU - Zicari, Roberto V.
AU - Brusseau, James
AU - Blomberg, Stig Nikolaj
AU - Christensen, Helle Collatz
AU - Coffee, Megan
AU - Ganapini, Marianna B.
AU - Gerke, Sara
AU - Gilbert, Thomas Krendl
AU - Hickman, Eleanore
AU - Hildt, Elisabeth
AU - Holm, Sune
AU - Kühne, Ulrich
AU - Madai, Vince I.
AU - Osika, Walter
AU - Spezzatti, Andy
AU - Schnebel, Eberhard
AU - Tithi, Jesmin Jahan
AU - Vetter, Dennis
AU - Westerlund, Magnus
AU - Wurth, Renee
AU - Amann, Julia
AU - Antun, Vegard
AU - Beretta, Valentina
AU - Bruneault, Frédérick
AU - Campano, Erik
AU - Düdder, Boris
AU - Gallucci, Alessio
AU - Goffi, Emmanuel
AU - Haase, Christoffer Bjerre
AU - Hagendorff, Thilo
AU - Kringen, Pedro
AU - Möslein, Florian
AU - Ottenheimer, Davi
AU - Ozols, Matiss
AU - Palazzani, Laura
AU - Petrin, Martin
AU - Tafur, Karin
AU - Tørresen, Jim
AU - Volland, Holger
AU - Kararigas, Georgios
PY - 2021/7
Y1 - 2021/7
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.
AB - 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.
U2 - 10.3389/fhumd.2021.673104
DO - 10.3389/fhumd.2021.673104
M3 - Journal article
VL - 3
JO - Frontiers in Human Dynamics
JF - Frontiers in Human Dynamics
SN - 2673-2726
M1 - 673104
ER -