Print Email Facebook Twitter Assessing Trustworthy AI in Times of COVID-19 Title Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients Author Allahabadi, H. (EY Netherlands) Amann, J. (ETH Zürich) Balot, I. (Center for Diplomatic & Strategic Studies) Beretta, A. (CNR) Binkley, C. (Hackensack Meridian Health) Bozenhard, J. (University of Oxford) Bruneault, F. (Cégep André-Laurendeau; Université du Québec) Brusseau, J. (Pace University) Umbrello, S. (TU Delft Ethics & Philosophy of Technology) Date 2022 Abstract This article’s main contributions are twofold: 1) to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. Subject Artificial intelligenceCOVID-19PandemicsMedical servicesEthicsRadiologyLungDeep learningcase studyethical tradeoffethicsexplainable AIhealthcarepandemicradiologytrusttrustworthy AIZ-Inspection® To reference this document use: http://resolver.tudelft.nl/uuid:afcae6a2-44ec-4d8f-b80f-d8fb8a35e871 DOI https://doi.org/10.1109/TTS.2022.3195114 ISSN 2637-6415 Source IEEE Transactions on Technology and Society, 3 (4), 272-289 Part of collection Institutional Repository Document type journal article Rights © 2022 H. Allahabadi, J. Amann, I. Balot, A. Beretta, C. Binkley, J. Bozenhard, F. Bruneault, J. Brusseau, S. Umbrello, More Authors Files PDF Assessing_Trustworthy_AI_ ... tients.pdf 964.09 KB Close viewer /islandora/object/uuid:afcae6a2-44ec-4d8f-b80f-d8fb8a35e871/datastream/OBJ/view