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145 results on '"Holm, Sune"'

1. “Metabolic fingerprints” of cachexia in lung cancer patients

Author

Frille, Armin, Arends, Jann, Abenavoli, Elisabetta M., Duke, Shaul A., Ferrara, Daria, Gruenert, Stefan, Hacker, Marcus, Hesse, Swen, Hofmann, Lukas, Holm, Sune H., Lund, Thomas B., Rullmann, Michael, Sandøe, Peter, Sciagrà, Roberto, Shiyam Sundar, Lalith Kumar, Tönjes, Anke, Wirtz, Hubert, Yu, Josef, Sabri, Osama, and Beyer, Thomas

Published
2024
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2. On (assessing) the fairness of risk score models

Author

Petersen, Eike, Ganz, Melanie, Holm, Sune Hannibal, and Feragen, Aasa

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, Statistics - Applications, Statistics - Methodology, Statistics - Machine Learning, 91B32, 62-XX, 91Gxx, 68T07, K.4.1, K.4.2, J.3, I.5, G.3
Abstract

Recent work on algorithmic fairness has largely focused on the fairness of discrete decisions, or classifications. While such decisions are often based on risk score models, the fairness of the risk models themselves has received considerably less attention. Risk models are of interest for a number of reasons, including the fact that they communicate uncertainty about the potential outcomes to users, thus representing a way to enable meaningful human oversight. Here, we address fairness desiderata for risk score models. We identify the provision of similar epistemic value to different groups as a key desideratum for risk score fairness. Further, we address how to assess the fairness of risk score models quantitatively, including a discussion of metric choices and meaningful statistical comparisons between groups. In this context, we also introduce a novel calibration error metric that is less sample size-biased than previously proposed metrics, enabling meaningful comparisons between groups of different sizes. We illustrate our methodology - which is widely applicable in many other settings - in two case studies, one in recidivism risk prediction, and one in risk of major depressive disorder (MDD) prediction.

Published
2023

3. How to Assess Trustworthy AI in Practice

Author

Zicari, Roberto V., Amann, Julia, Bruneault, Frédérick, Coffee, Megan, Düdder, Boris, Hickman, Eleanore, Gallucci, Alessio, Gilbert, Thomas Krendl, Hagendorff, Thilo, van Halem, Irmhild, Hildt, Elisabeth, Holm, Sune, Kararigas, Georgios, Kringen, Pedro, Madai, Vince I., Mathez, Emilie Wiinblad, Tithi, Jesmin Jahan, Vetter, Dennis, Westerlund, Magnus, and Wurth, Renee

Subjects
Computer Science - Computers and Society
Abstract

This report is a methodological reflection on Z-Inspection$^{\small{\circledR}}$. Z-Inspection$^{\small{\circledR}}$ is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system., Comment: On behalf of the Z-Inspection$^{\small{\circledR}}$ initiative (2022)

Published
2022
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4. Lessons Learned from Assessing Trustworthy AI in Practice

Author

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, and Zicari, Roberto V.

Published
2023
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5. 2022 Roadmap on Neuromorphic Computing and Engineering

Author

Christensen, Dennis V., Dittmann, Regina, Linares-Barranco, Bernabé, Sebastian, Abu, Gallo, Manuel Le, Redaelli, Andrea, Slesazeck, Stefan, Mikolajick, Thomas, Spiga, Sabina, Menzel, Stephan, Valov, Ilia, Milano, Gianluca, Ricciardi, Carlo, Liang, Shi-Jun, Miao, Feng, Lanza, Mario, Quill, Tyler J., Keene, Scott T., Salleo, Alberto, Grollier, Julie, Marković, Danijela, Mizrahi, Alice, Yao, Peng, Yang, J. Joshua, Indiveri, Giacomo, Strachan, John Paul, Datta, Suman, Vianello, Elisa, Valentian, Alexandre, Feldmann, Johannes, Li, Xuan, Pernice, Wolfram H. P., Bhaskaran, Harish, Furber, Steve, Neftci, Emre, Scherr, Franz, Maass, Wolfgang, Ramaswamy, Srikanth, Tapson, Jonathan, Panda, Priyadarshini, Kim, Youngeun, Tanaka, Gouhei, Thorpe, Simon, Bartolozzi, Chiara, Cleland, Thomas A., Posch, Christoph, Liu, Shih-Chii, Panuccio, Gabriella, Mahmud, Mufti, Mazumder, Arnab Neelim, Hosseini, Morteza, Mohsenin, Tinoosh, Donati, Elisa, Tolu, Silvia, Galeazzi, Roberto, Christensen, Martin Ejsing, Holm, Sune, Ielmini, Daniele, and Pryds, N.

Subjects
Computer Science - Emerging Technologies, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Materials Science
Abstract

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

Published
2021
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13. Data‐driven decisions about individual patients: The case of medical AI.

Author

Holm, Sune

Subjects
*DECISION support systems, *STATISTICAL models, *ARTIFICIAL intelligence, *EVIDENCE-based medicine, *INDIVIDUALIZED medicine, *ALGORITHMS, *HEALTH care rationing
Abstract

There are high hopes that clinical decisions can be improved by adopting algorithms trained to estimate the likelihood that a patient suffers a condition C. Introducing work on the epistemic value of purely statistical evidence in legal epistemology I show that a certain type of AI devices for making medical decisions about persons rely on purely statistical evidence and that it raises an important question about the appropriateness of relying on such devices for allocating health resources. If the argument I present is sound, then it suggests a radical rethinking of the use of prevalent types of AI devices as well as the use of statistical evidence in medical practice more generally. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Introduction

Author

Holt, Louisa, primary, Holm, Sune, additional, and Serban, Maria, additional

Published
2020
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19. The persistence of persons

Author

Holm, Sune

Subjects
BD236.H7, Identity (Psychology), Identity (Philosophical concept), Persons
Abstract

In this thesis I discuss what it takes for one and the same person to exist at different times. I begin by presenting how to understand this question as a philosophical question and argue that we should accept a so-called psychological criterion of personal identity. In the course of the thesis I discuss what I take to be the most pertinent objections facing accepting a psychological criterion. I consider the role of prudential concern in arguments in favour of the psychological criterion and defend it against a recent criticism. I then turn to the complaint that the psychological criterion is viciously circular in that it accounts for personal identity in terms of psychological relations which presuppose personal identity and defend the appeal to the central notion of quasi-memory. An important part of the thesis concerns how a proponent of the psychological criterion can deal with problems arising from the possibility that what is plausibly thought to be two distinct persons may stand in the relation of psychological continuity to what is plausibly claimed to be a single person. I argue that a proponent of the psychological criterion should accept the thesis that numerical identity is temporary and provide a defence of the temporary identity thesis. In the final chapters of the thesis I discuss how a proponent of the psychological criterion can accommodate the view that persons are enduring material beings, paradigmatically human organisms and submit that accepting the temporary identity thesis not only provides an attractive way of maintaining the claim that paradigm persons are identical with human organisms, but also sheds light on the role of personal identity in memory knowledge and prudential concern.

Published
2006

26. Bias in context: What to do when complete bias removal is not an option

Author

Holm, Sune, Petersen, Eike, Ganz, Melanie, and Feragen, Aasa

Subjects
Bias
Abstract

It is widely recognized that machine learning algorithms may be biased in the sense that they perform worse on some demographic groups than others. This motivates algorithmic development to remove algorithmic bias, which in turn might lead to a hope—even an expectation—that algorithmic bias can be mitigated or removed (1). In this short comment, we make three points to qualify Wang et al.’s suggestion: 1) It may not be possible for algorithms to perform equally well across groups on all measures, 2) which inequalities count as morally unacceptable bias is an ethical question, and 3) the answer to the ethical question will vary across decision contexts.

Published
2023

29. 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, Himanshi, primary, Amann, Julia, additional, Balot, Isabelle, additional, Beretta, Andrea, additional, Binkley, Charles, additional, Bozenhard, Jonas, additional, Bruneault, Frederick, additional, Brusseau, James, additional, Candemir, Sema, additional, Cappellini, Luca Alessandro, additional, Chakraborty, Subrata, additional, Cherciu, Nicoleta, additional, Cociancig, Christina, additional, Coffee, Megan, additional, Ek, Irene, additional, Espinosa-Leal, Leonardo, additional, Farina, Davide, additional, Fieux-Castagnet, Genevieve, additional, Frauenfelder, Thomas, additional, Gallucci, Alessio, additional, Giuliani, Guya, additional, Golda, Adam, additional, van Halem, Irmhild, additional, Hildt, Elisabeth, additional, Holm, Sune, additional, Kararigas, Georgios, additional, Krier, Sebastien A., additional, Kuhne, Ulrich, additional, Lizzi, Francesca, additional, Madai, Vince I., additional, Markus, Aniek F., additional, Masis, Serg, additional, Mathez, Emilie Wiinblad, additional, Mureddu, Francesco, additional, Neri, Emanuele, additional, Osika, Walter, additional, Ozols, Matiss, additional, Panigutti, Cecilia, additional, Parent, Brendan, additional, Pratesi, Francesca, additional, Moreno-Sanchez, Pedro A., additional, Sartor, Giovanni, additional, Savardi, Mattia, additional, Signoroni, Alberto, additional, Sormunen, Hanna-Maria, additional, Spezzatti, Andy, additional, Srivastava, Adarsh, additional, Stephansen, Annette F., additional, Theng, Lau Bee, additional, Tithi, Jesmin Jahan, additional, Tuominen, Jarno, additional, Umbrello, Steven, additional, Vaccher, Filippo, additional, Vetter, Dennis, additional, Westerlund, Magnus, additional, Wurth, Renee, additional, and Zicari, Roberto V., additional

Published
2022
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33. 2022 roadmap on neuromorphic computing and engineering

Author

Christensen, Dennis V, primary, Dittmann, Regina, additional, Linares-Barranco, Bernabe, additional, Sebastian, Abu, additional, Le Gallo, Manuel, additional, Redaelli, Andrea, additional, Slesazeck, Stefan, additional, Mikolajick, Thomas, additional, Spiga, Sabina, additional, Menzel, Stephan, additional, Valov, Ilia, additional, Milano, Gianluca, additional, Ricciardi, Carlo, additional, Liang, Shi-Jun, additional, Miao, Feng, additional, Lanza, Mario, additional, Quill, Tyler J, additional, Keene, Scott T, additional, Salleo, Alberto, additional, Grollier, Julie, additional, Marković, Danijela, additional, Mizrahi, Alice, additional, Yao, Peng, additional, Yang, J Joshua, additional, Indiveri, Giacomo, additional, Strachan, John Paul, additional, Datta, Suman, additional, Vianello, Elisa, additional, Valentian, Alexandre, additional, Feldmann, Johannes, additional, Li, Xuan, additional, Pernice, Wolfram H P, additional, Bhaskaran, Harish, additional, Furber, Steve, additional, Neftci, Emre, additional, Scherr, Franz, additional, Maass, Wolfgang, additional, Ramaswamy, Srikanth, additional, Tapson, Jonathan, additional, Panda, Priyadarshini, additional, Kim, Youngeun, additional, Tanaka, Gouhei, additional, Thorpe, Simon, additional, Bartolozzi, Chiara, additional, Cleland, Thomas A, additional, Posch, Christoph, additional, Liu, ShihChii, additional, Panuccio, Gabriella, additional, Mahmud, Mufti, additional, Mazumder, Arnab Neelim, additional, Hosseini, Morteza, additional, Mohsenin, Tinoosh, additional, Donati, Elisa, additional, Tolu, Silvia, additional, Galeazzi, Roberto, additional, Christensen, Martin Ejsing, additional, Holm, Sune, additional, Ielmini, Daniele, additional, and Pryds, N, additional

Published
2022
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34. To explain or not to explain? Artificial intelligence explainability in clinical decision support systems

Author

Amann, Julia, Vetter, Dennis, Blomberg, Stig Nikolaj, Christensen, Helle Collatz, Coffee, Megan, Gerke, Sara, Gilbert, Thomas K., Hagendorff, Thilo, Holm, Sune, Livne, Michelle, Spezzatti, Andy, Strümke, Inga, Zicari, Roberto V., and Madai, Vince Istvan

Abstract

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper pres- ents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice., PLOS Digital Health, 1 (2)

Published
2022

36. Co-Design of a Trustworthy AI System in Healthcare: Deep Learning Based Skin Lesion Classifier

Author

Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan A., Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Krendl Gilbert, Thomas, Gottfrois, Philippe, Goffi, Emmanuel, Bjerre Haase, Christoffer, Hagendorff, Thilo, Hickman, Eleanore, Hildt, Elisabeth, Holm, Sune, Kringen, Pedro, Kühne, Ulrich, Lucieri, Adriano, Madai, Vince I., Moreno-Sánchez, Pedro A., Medlicott, Oriana, Ozols, Matiss, Schnebel, Eberhard, Spezzatti, Andy, Tithi, Jesmin J., Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Apollo - University of Cambridge Repository, Security, and Eindhoven MedTech Innovation Center

Subjects
Z-inspection®1, Artificial intelligence, Healthcare, Trustworthy AI, Ethics, Malignant melanoma, Z-inspection ®1, ethical co-design, trustworthy AI Co-design, malignant melanoma, healthcare, Social Sciences, artificial intelligence, ethics, Basic Behavioral and Social Science, 46 Information and Computing Sciences, trustworthy AI, Behavioral and Social Science, ddc:610, ddc:004, 33 Built Environment and Design, 3303 Design
Abstract

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments., Frontiers in Human Dynamics, 3, ISSN:2673-2726

Published
2021
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37. On assessing trustworthy AI in healthcare:Best practice for machine learning as a supportive tool to recognize cardiac arrest in emergency calls

Author

Zicari, Roberto V., Brusseau, James, Blomberg, Stig Nikolaj, Christensen, Helle Collatz, Coffee, Megan, Ganapini, Marianna B., Gerke, Sara, Gilbert, Thomas Krendl, Hickman, Eleanore, Hildt, Elisabeth, Holm, Sune, Kühne, Ulrich, Madai, Vince I., Osika, Walter, Spezzatti, Andy, Schnebel, Eberhard, Tithi, Jesmin Jahan, Vetter, Dennis, Westerlund, Magnus, Wurth, Renee, Amann, Julia, Antun, Vegard, Beretta, Valentina, Bruneault, Frédérick, Campano, Erik, Düdder, Boris, Gallucci, Alessio, Goffi, Emmanuel, Haase, Christoffer Bjerre, Hagendorff, Thilo, Kringen, Pedro, Möslein, Florian, Ottenheimer, Davi, Ozols, Matiss, Palazzani, Laura, Petrin, Martin, Tafur, Karin, Tørresen, Jim, Volland, Holger, and Kararigas, Georgios

Subjects
ComputingMethodologies_PATTERNRECOGNITION, GeneralLiterature_MISCELLANEOUS
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.

Published
2021

39. On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

Author

Zicari, Roberto V., primary, Brusseau, James, additional, Blomberg, Stig Nikolaj, additional, Christensen, Helle Collatz, additional, Coffee, Megan, additional, Ganapini, Marianna B., additional, Gerke, Sara, additional, Gilbert, Thomas Krendl, additional, Hickman, Eleanore, additional, Hildt, Elisabeth, additional, Holm, Sune, additional, Kühne, Ulrich, additional, Madai, Vince I., additional, Osika, Walter, additional, Spezzatti, Andy, additional, Schnebel, Eberhard, additional, Tithi, Jesmin Jahan, additional, Vetter, Dennis, additional, Westerlund, Magnus, additional, Wurth, Renee, additional, Amann, Julia, additional, Antun, Vegard, additional, Beretta, Valentina, additional, Bruneault, Frédérick, additional, Campano, Erik, additional, Düdder, Boris, additional, Gallucci, Alessio, additional, Goffi, Emmanuel, additional, Haase, Christoffer Bjerre, additional, Hagendorff, Thilo, additional, Kringen, Pedro, additional, Möslein, Florian, additional, Ottenheimer, Davi, additional, Ozols, Matiss, additional, Palazzani, Laura, additional, Petrin, Martin, additional, Tafur, Karin, additional, Tørresen, Jim, additional, Volland, Holger, additional, and Kararigas, Georgios, additional

Published
2021
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