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Lessons Learned from Assessing Trustworthy AI in Practice

Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Scaling up the lattice dynamics of amorphous materials by orders of magnitude

arXiv

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arXiv 2020年

作者： Kriuchevskyi, Ivan Palyulin, Vladimir V. Milkus, Rico Elder, Robert M. Sirk, Timothy W. Zaccone, Alessio Department of Physics "A. Pontremoli" University of Milan via Celoria 16 Milan20133 Italy Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow121205 Russia Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Polymers Branch U.S. Army Research Laboratory Aberdeen Proving Ground MD United States Bennett Aerospace Inc. CaryNC27518 United States Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD20903 United States

We generalise the non-affine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic resolution, we propose an extension of the Kernel Polynomial Method (KPM) for the computation of the vibrational density of states (VDOS) and the eigenmodes, including the Γ-correlator of the affine force-field, which is a key ingredient of lattice-dynamic calculations of viscoelasticity. We show that the results converge well to the solution obtained by direct diagonalization (DD) of the Hessian (dynamical) matrix. As is well known, the DD approach has prohibitively high computational requirements for systems with N = 104 atoms or larger. Instead, the KPM approach developed here allows one to scale up lattice dynamic calculations of real materials up to 106 atoms, with a hugely more favorable (linear) scaling of computation time and memory consumption with N. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

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Vec2SPARQL: Integrating SPARQL queries and knowledge graph embeddings 11

Vec2SPARQL: Integrating SPARQL queries and knowledge graph e...

引用

11th International Conference Semantic Web Applications and Tools for Life sciences, SWAT4LS 2018

作者： Kulmanov, Maxat Kafkas, Senay Karwath, Andreas Malic, Alexander Gkoutos, Georgios V Dumontier, Michel Hoehndorf, Robert Computer Electrical and Mathematical Science and Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre for Computational Biology University of Birmingham Birmingham United Kingdom Institute of Data Science Maastricht University Maastricht Netherlands

Recent developments in machine learning have led to a rise of large number of methods for extracting features from structured data. The features are represented as vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://***/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://***/. © 2018 CEUR Workshop Proceedings. All rights reserved.

关键词： Vector spaces

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Evolutionary Structural Optimization Algorithm Based on FFT-JVIE Solver for Inverse Design of Wave Devices

Evolutionary Structural Optimization Algorithm Based on FFT-...

引用

engineering and Telecommunication (EnT-MIPT)

作者： Iurii B. Minin Egor E. Nuzhin Alexey I. Boyko Mikhail S. Litsarev Ivan V. Oseledets Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (纸本)9781728104331

We propose a topology optimization method with a target function based on FFT-JVIE solver. It allows to find an optimal structure in a 3D space for homogeneous objects (lenses and metallic reflectors). Such examples as glass lenses, parabolic reflectors are considered. Numerical experiments show that discrete gradient algorithm converges much faster than greedy algorithm for all considered cases.

关键词： Optimization Two dimensional displays Three-dimensional displays Topology Tensors Lenses

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Optimizing Laser Pulses for Narrowband Inverse Compton Sources in the High-Intensity Regime

arXiv

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arXiv 2019年

作者： Seipt, Daniel Yu. Kharin, Vasily Rykovanov, Sergey G. Helmholtz-Institut Jena Fröbelstieg 3 Jena07743 Germany Center for Ultrafast Optical Science University of Michigan Ann Arbor MI48109 United States Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobel Str. 3 Skolkovo Russia

Scattering of ultraintense short laser pulses off relativistic electrons allows one to generate a large number of X- or gamma-ray photons with the expense of the spectral width—temporal pulsing of the laser inevitable leads to considerable spectral broadening. In this Letter, we describe a simple method to generate optimized laser pulses that compensate the nonlinear spectrum broadening, and can be thought of as a superposition of two oppositely linearly chirped pulses delayed with respect to each other. We develop a simple analytical model that allow us to predict the optimal parameters of such a two-pulse—the delay, amount of chirp and relative phase—for generation of a narrowband γ-ray spectrum. Our predictions are confirmed by numerical optimization and simulations including 3D effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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Optimizing Laser Pulses for Narrow-Band Inverse Compton Sources in the High-Intensity Regime

引用

Physical Review Letters 2019年第20期122卷 204802-204802页

作者： Daniel Seipt Vasily Yu. Kharin Sergey G. Rykovanov Helmholtz-Institut Jena Fröbelstieg 3 07743 Jena Germany Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan 48109 USA Research Institute Moscow R&D Lab Moscow Bersenevskaya nab. 6 119072 Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobel Str. 3 Skolkovo Russia

Scattering of ultraintense short laser pulses off relativistic electrons allows one to generate a large number of X- or gamma-ray photons with the expense of the spectral width—temporal pulsing of the laser inevitable leads to considerable spectral broadening. In this Letter, we describe a simple method to generate optimized laser pulses that compensate the nonlinear spectrum broadening and can be thought of as a superposition of two oppositely linearly chirped pulses delayed with respect to each other. We develop a simple analytical model that allows us to predict the optimal parameters of such a two-pulse—the delay, amount of chirp, and relative phase—for generation of a narrow-band γ-ray spectrum. Our predictions are confirmed by numerical optimization and simulations including three-dimensional effects.

关键词： Gamma-ray generation in plasmas High intensity laser-plasma interactions Strong electromagnetic field effects Radiation from moving charges

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Detecting anomalous citation groups in journal networks

arXiv

引用

arXiv 2020年

作者： Kojaku, Sadamori Livan, Giacomo Masuda, Naoki Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science University College London LondonWC1E 6EA United Kingdom Systemic Risk Centre London School of Economics and Political Science LondonWC2A 2AE United Kingdom Department of Mathematics University at Buffalo State University of New York BuffaloNY United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-2900 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

The ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a "citation cartel" of journals. "Citation cartel" behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—-named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate. Copyright © 2020, The Authors. All rights reserved.

关键词： Publishing

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Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

arXiv

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arXiv 2024年

作者： Evans, Matthew L. Bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar B. Beltrán, Daniel Blokhin, Evgeny Boland, Tara M. Balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez Eckert, Hagen Eimre, Kristjan Montero, María Elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro Baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu Bonella, Sara Botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis Edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. © 2024, CC BY.

关键词： Electronic data interchange

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The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

arXiv

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arXiv 2022年

作者： Bhattacharyay, Shubhayu Milosevic, Ioan Wilson, Lindsay Menon, David K. Stevens, Robert D. Steyerberg, Ewout W. Nelson, David W. Ercole, Ari Åkerlund, Cecilia Amrein, Krisztina Andelic, Nada Andreassen, Lasse Anke, Audny Antoni, Anna Audibert, Gérard Azouvi, Philippe Azzolini, Maria Luisa Bartels, Ronald Barzó, Pál Beauvais, Romuald Beer, Ronny Bellander, Bo-Michael Belli, Antonio Benali, Habib Berardino, Maurizio Beretta, Luigi Blaabjerg, Morten Bragge, Peter Brazinova, Alexandra Brinck, Vibeke Brooker, Joanne Brorsson, Camilla Buki, Andras Bullinger, Monika Cabeleira, Manuel Caccioppola, Alessio Calappi, Emiliana Calvi, Maria Rosa Cameron, Peter Lozano, Guillermo Carbayo Carbonara, Marco Cavallo, Simona Chevallard, Giorgio Chieregato, Arturo Citerio, Giuseppe Clusmann, Hans Coburn, Mark Coles, Jonathan Cooper, Jamie D. Correia, Marta Čović, Amra Curry, Nicola Czeiter, Endre Czosnyka, Marek Dahyot-Fizelier, Claire Dark, Paul Dawes, Helen De Keyser, Véronique Degos, Vincent Della Corte, Francesco den Boogert, Hugo Depreitere, Bart Đilvesi, Đula Dixit, Abhishek Donoghue, Emma Dreier, Jens Dulière, Guy-Loup Esser, Patrick Ezer, Erzsébet Fabricius, Martin Feigin, Valery L. Foks, Kelly Frisvold, Shirin Furmanov, Alex Gagliardo, Pablo Galanaud, Damien Gantner, Dashiell Gao, Guoyi George, Pradeep Ghuysen, Alexandre Giga, Lelde Glocker, Ben Golubovic, Jagoš Gomez, Pedro A. Gratz, Johannes Gravesteijn, Benjamin Grossi, Francesca Gruen, Russell L. Gupta, Deepak Haagsma, Juanita A. Haitsma, Iain Helbok, Raimund Helseth, Eirik Horton, Lindsay Huijben, Jilske Hutchinson, Peter J. Jacobs, Bram Jankowski, Stefan Jarrett, Mike Jiang, Ji-Yao Johnson, Faye Jones, Kelly Karan, Mladen Kolias, Angelos G. Kompanje, Erwin Kondziella, Daniel Kornaropoulos, Evgenios Koskinen, Lars-Owe Kovács, Noémi Kowark, Ana Lagares, Alfonso Lanyon, Linda Laureys, Steven Lecky, Fiona Ledoux, Didier Lefering, Rolf Legrand, Valerie Lejeune, Aurelie Levi, Leon Lightfoot, Roger Lingsma, Hester Maas, Andrew I.R. Castaño-León, Ana M. Maegele, Marc Majdan, M Division of Anaesthesia University of Cambridge Addenbrooke's Hospital Cambridge United Kingdom Division of Neurosurgery Department of Clinical Neurosciences Addenbrooke's Hospital University of Cambridge Cambridge United Kingdom Laboratory of Computational Intensive Care Medicine Johns Hopkins University BaltimoreMD United States Division of Psychology University of Stirling Stirling United Kingdom Department of Anesthesiology and Critical Care Medicine Johns Hopkins University BaltimoreMD United States Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Physiology and Pharmacology Section for Perioperative Medicine and Intensive Care Karolinska Institutet Stockholm Sweden Cambridge Centre for Artificial Intelligence in Medicine Cambridge United Kingdom Division of Surgery and Clinical Neuroscience Department of Physical Medicine and Rehabilitation Oslo University Hospital University of Oslo Oslo Norway Department of Neurosurgery University Hospital Northern Norway Tromso Norway Department of Physical Medicine and Rehabilitation University Hospital Northern Norway Tromso Norway Trauma Surgery Medical University Vienna Vienna Austria Department of Anesthesiology & Intensive Care University Hospital Nancy Nancy France Raymond Poincare Hospital Assistance Publique - Hopitaux de Paris Paris France Department of Anesthesiology & Intensive Care S Raffaele University Hospital Milan Italy Department of Neurosurgery Radboud University Medical Center Nijmegen Netherlands Department of Neurosurgery University of Szeged Szeged Hungary International Projects Management ARTTIC Munchen Germany Department of Neurology Neurological Intensive Care Unit Medical University of Innsbruck Innsbruck Austria Department of Neurosurgery & Anesthesia & Intensive Care Medicine Karolinska University Hospital Stockholm Sweden NIHR Surgical Reconstruction and Microbiology Research Centre Birmingham United Kingdom Anesthesi

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale - Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74 - 0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54% - 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE a

关键词： Neural network models

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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

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

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. © 2020 IEEE.

关键词： COVID-19

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