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 -