Frontiers in Human Dynamics (Jul 2021)
On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls
- Roberto V. Zicari,
- Roberto V. Zicari,
- James Brusseau,
- Stig Nikolaj Blomberg,
- Helle Collatz Christensen,
- Megan Coffee,
- Marianna B. Ganapini,
- Sara Gerke,
- Thomas Krendl Gilbert,
- Eleanore Hickman,
- Elisabeth Hildt,
- Sune Holm,
- Ulrich Kühne,
- Vince I. Madai,
- Vince I. Madai,
- Vince I. Madai,
- Walter Osika,
- Andy Spezzatti,
- Eberhard Schnebel,
- Jesmin Jahan Tithi,
- Dennis Vetter,
- Magnus Westerlund,
- Renee Wurth,
- Julia Amann,
- Vegard Antun,
- Valentina Beretta,
- Frédérick Bruneault,
- Erik Campano,
- Boris Düdder,
- Alessio Gallucci,
- Emmanuel Goffi,
- Christoffer Bjerre Haase,
- Thilo Hagendorff,
- Pedro Kringen,
- Florian Möslein,
- Davi Ottenheimer,
- Matiss Ozols,
- Laura Palazzani,
- Martin Petrin,
- Martin Petrin,
- Karin Tafur,
- Jim Tørresen,
- Holger Volland,
- Georgios Kararigas
Affiliations
- Roberto V. Zicari
- Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland
- Roberto V. Zicari
- Data Science Graduate School, Seoul National University, Seoul, South Korea
- James Brusseau
- Philosophy Department, Pace University, New York, NY, United States
- Stig Nikolaj Blomberg
- University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark
- Helle Collatz Christensen
- University of Copenhagen, Copenhagen Emergency Medical Services, Copenhagen, Denmark
- Megan Coffee
- Department of Medicine and Division of Infectious Diseases and Immunology, NYU Grossman School of Medicine, New York, NY, United States
- Marianna B. Ganapini
- Montreal AI Ethics Institute, Canada and Union College, New York, NY, United States
- Sara Gerke
- Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School, Berkeley, CA, United States
- Thomas Krendl Gilbert
- Center for Human-Compatible AI, University of California, Berkeley, CA, United States
- Eleanore Hickman
- Faculty of Law, University of Cambridge, Cambridge, United Kingdom
- Elisabeth Hildt
- 0Center for the Study of Ethics in the Professions, Illinois Institute of Technology Chicago, Chicago, IL, United States
- Sune Holm
- 1Department of Food and Resource Economics, Faculty of Science University of Copenhagen, Copenhagen, Denmark
- Ulrich Kühne
- 2Hautmedizin, Bad Soden, Germany
- Vince I. Madai
- 3CLAIM - Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Vince I. Madai
- 4QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Charité Universitätsmedizin Berlin, Berlin, Germany
- Vince I. Madai
- 5School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, London, United Kingdom
- Walter Osika
- 6Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Andy Spezzatti
- 7Industrial Engineering and Operation Research, University of California, Berkeley, CA, United States
- Eberhard Schnebel
- 8Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany
- Jesmin Jahan Tithi
- 9Parallel Computing Labs, Intel, Santa Clara, CA, United States
- Dennis Vetter
- 8Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany
- Magnus Westerlund
- Artificial Intelligence, Arcada University of Applied Sciences, Helsinki, Finland
- Renee Wurth
- 0Fitbiomics, New York, NY, United States
- Julia Amann
- 1Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
- Vegard Antun
- 2Department of Mathematics, University of Oslo, Oslo, Norway
- Valentina Beretta
- 3Department of Economics and Management, Università degli studi di Pavia, Pavia, Italy
- Frédérick Bruneault
- 4École des médias, Université du Québec à Montréal and Philosophie, Collège André-Laurendeau, Québec, QC, Canada
- Erik Campano
- 5Department of Informatics, Umeå University, Umeå, Sweden
- Boris Düdder
- 6Department of Computer Science (DIKU), University of Copenhagen (UCPH), Copenhagen, Denmark
- Alessio Gallucci
- 7Department of Mathematics and Computer Science Eindhoven University of Technology, Eindhoven, Netherlands
- Emmanuel Goffi
- 8Observatoire Ethique and Intelligence Artificielle de l’Institut Sapiens, Paris-Cachan, France
- Christoffer Bjerre Haase
- 9Section for Health Service Research and Section for General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Thilo Hagendorff
- 0Cluster of Excellence "Machine Learning: New Perspectives for Science", University of Tuebingen, Tuebingen, Germany
- Pedro Kringen
- 8Frankfurt Big Data Lab, Goethe University, Frankfurt, Germany
- Florian Möslein
- 1Institute of the Law and Regulation of Digitalization, Philipps-University Marburg, Philipps, Germany
- Davi Ottenheimer
- 2Inrupt, San Francisco, CA, United States
- Matiss Ozols
- 3University of Manchester and Wellcome Sanger Institute, Cambridge, United Kingdom
- Laura Palazzani
- 4Philosophy of Law, LUMSA University, Rome, Italy
- Martin Petrin
- 5Law Department, Western University, London, ON, Canada
- Martin Petrin
- 6Faculty of Laws, University College London, London, United Kingdom
- Karin Tafur
- 7Independent AI Researcher (Law and Ethics) and Legal Tech Entrepreneur, Barcelona, Spain
- Jim Tørresen
- 8Department of Informatics, University of Oslo, Oslo, Norway
- Holger Volland
- 9Head of Community and Communications, Z-Inspection®Initiative, london
- Georgios Kararigas
- 0Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- DOI
- https://doi.org/10.3389/fhumd.2021.673104
- Journal volume & issue
-
Vol. 3
Abstract
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.
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