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1. Effect of maximum exercise on left ventricular deformation and its correlation with cardiopulmonary exercise capacity in competitive athletes

Author

J. Kandels, S. Stöbe, A. Kogel, P. Hepp, H. Riepenhof, J. N. Droste, T. Stoeggl, R. P. Marshall, U. Rudolph, U. Laufs, S. Fikenzer, and A. Hagendorff

Subjects
Echocardiography, Athletes, Cardiopulmonary exercise test, Deformation, Longitudinal strain, Work index, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background Global longitudinal strain (GLS) and global myocardial work index (GWI) allow early detection of subclinical changes in left ventricular (LV) systolic function. The aim of the study was to investigate the immediate effects of maximum physical exercise by different exercise testing methods on early post exercise LV deformation parameters in competitive athletes and to analyze their correlation with cardiopulmonary exercise capacity. Methods To reach maximum physical exercise, cardiopulmonary exercise testing (CPET) was performed by semi-recumbent ergometer in competitive handball players (n = 13) and by treadmill testing in competitive football players (n = 19). Maximum oxygen uptake (VO2max) indexed to body weight (relative VO2max) was measured in all athletes. Transthoracic echocardiography and blood pressure measurements were performed at rest and 5 min after CPET in all athletes. GLS, GWI and their changes before and after CPET (ΔGLS, ΔGWI) were correlated with (relative) VO2max. Results In handball and football players, GLS and GWI did not differ significantly before and after CPET. There were no significant correlations between GLS and relative VO2max, but moderate correlations were found between ΔGWI and relative VO2max in handball (r = 0.631; P = 0.021) and football players (r = 0.592; P = 0.008). Furthermore, handball (46.7 ml/min*kg ± 4.7 ml/min*kg vs. 37.4 ml/min*kg ± 4.2; P = 0.004) and football players (58.3 ml/min*kg ± 3.7 ml/min*kg vs. 49.7 ml/min*kg ± 6.8; P = 0.002) with an increased ΔGWI after CPET showed a significant higher relative VO2max. Conclusion Maximum physical exercise has an immediate effect on LV deformation, irrespective of the used testing method. The correlation of relative VO2max with ΔGWI in the early post exercise period, identifies ΔGWI as an echocardiographic parameter for characterizing the current individual training status of athletes.

Published
2023
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2. SARS-CoV2 infection: functional and morphological cardiopulmonary changes in elite handball players

Author

S. Fikenzer, A. Kogel, C. Pietsch, D. Lavall, St. Stöbe, U. Rudolph, U. Laufs, P. Hepp, and A. Hagendorff

Subjects
Medicine, Science
Abstract

Abstract There is increasing evidence of cardiac involvement post-SARS-CoV-2 infections in symptomatic as well as in oligo- and asymptomatic athletes. This study aimed to characterize the possible early effects of SARS-CoV-2 infections on myocardial morphology and cardiopulmonary function in athletes. Eight male elite handball players (27 ± 3.5 y) with past SARS-CoV-2 infection were compared with four uninfected teammates (22 ± 2.6 y). Infected athletes were examined 19 ± 7 days after the first positive PCR test. Echocardiographic assessment of the global longitudinal strain under resting conditions was not significantly changed (− 17.7% vs. − 18.1%). However, magnetic resonance imaging showed minor signs of acute inflammation/oedema in all infected athletes (T2-mapping: + 4.1 ms, p = 0.034) without reaching the Lake-Louis criteria. Spiroergometric analysis showed a significant reduction in VO2max (− 292 ml/min, − 7.0%), oxygen pulse (− 2.4 ml/beat, − 10.4%), and respiratory minute volume (VE) (− 18.9 l/min, − 13.8%) in athletes with a history of SARS-CoV2 infection (p

Published
2021
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3. Mapping the Ethics of Generative AI: A Comprehensive Scoping Review

Author

Hagendorff, Thilo

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract

The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios.

Published
2024
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5. Echocardiographic assessment of left atrial appendage morphology and function—an expert proposal by the German Working Group of Cardiovascular Ultrasound

Author

Hagendorff, Andreas, Stöbe, Stephan, Helfen, Andreas, Knebel, Fabian, Altiok, Ertunc, Beckmann, Stephan, Bekfani, Tarek, Binder, Thomas, Ewers, Aydan, Hamadanchi, Ali, Freyhaus, Henrik ten, Groscheck, Thomas, Haghi, Dariush, Knierim, Jan, Kruck, Sebastian, Lenk, Karsten, Merke, Nicolas, Pfeiffer, Dietrich, Dorta, Elena Romero, Ruf, Tobias, Sinning, Christoph, Wunderlich, Nina C., Brandt, Roland, and Ewen, Sebastian

Published
2024
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6. Echocardiographic assessment of atrial, ventricular, and valvular function in patients with atrial fibrillation—an expert proposal by the german working group of cardiovascular ultrasound

Author

Hagendorff, Andreas, Stöbe, Stephan, Helfen, Andreas, Knebel, Fabian, Altiok, Ertunc, Beckmann, Stephan, Bekfani, Tarek, Binder, Thomas, Ewers, Aydan, Hamadanchi, Ali, ten Freyhaus, Henrik, Groscheck, Thomas, Haghi, Dariush, Knierim, Jan, Kruck, Sebastian, Lenk, Karsten, Merke, Nicolas, Pfeiffer, Dietrich, Dorta, Elena Romero, Ruf, Tobias, Sinning, Christoph, Wunderlich, Nina C., Brandt, Roland, and Ewen, Sebastian

Published
2024
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8. Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms

Author

Meding, Kristof and Hagendorff, Thilo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call "fairness hacking" for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in fair AI practices. We introduce two different categories of fairness hacking in reference to the established concept of p-hacking. The first category, intra-metric fairness hacking, describes the misuse of a particular metric by adding or removing sensitive attributes from the analysis. In this context, countermeasures that have been developed to prevent or reduce p-hacking can be applied to similarly prevent or reduce fairness hacking. The second category of fairness hacking is inter-metric fairness hacking. Inter-metric fairness hacking is the search for a specific fair metric with given attributes. We argue that countermeasures to prevent or reduce inter-metric fairness hacking are still in their infancy. Finally, we demonstrate both types of fairness hacking using real datasets. Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications.

Published
2023
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10. Deep vein thrombosis in patients with stroke or transient ischemic attack presenting with patent foramen ovale: a retrospective observational study

Author

Charlotte Huber, Stephan Stöbe, Andreas Hagendorff, Katja Sibylle Mühlberg, Karl-Titus Hoffmann, Berend Isermann, Rolf Wachter, Nikolaus von Dercks, Richard Schmidt, Johann Otto Pelz, and Dominik Michalski

Subjects
Deep vein thrombosis, Patent foramen ovale, Ischemic stroke, Transient ischemic attack, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective Deep vein thrombosis (DVT) is discussed as a source of embolism for cerebral ischemia in the presence of patent foramen ovale (PFO). However, previous studies reported varying rates of DVT in stroke patients, and recommendations for screening are lacking. This study aimed to characterize patients with stroke or transient ischemic attack (TIA) and concomitant PFO and explore the rate of DVT and associated parameters. Methods Medical records were screened for patients with stroke or TIA and echocardiographic evidence of PFO. Concomitant DVT was identified according to compression ultrasonography of the lower limbs. A variety of demographic, clinical, and laboratory parameters, the RoPE and Wells scores were compared between patients with and without DVT. Results Three-hundred-thirty-nine patients (mean age 61.2 ± 15.4 years, 61.1% male) with stroke or TIA and PFO, treated between 01/2015 and 12/2020, were identified. Stroke and TIA patients did not differ for demographic and vascular risk factors. DVT was found in 17 cases out of 217 (7.8%) with compression ultrasonography. DVT was associated with a history of DVT, cancer, previous immobilization, calf compression pain, calf circumference difference, and a few laboratory abnormalities, e.g., increased D-dimer. A multivariate regression model with stepwise backward selection identified the Wells score (odds ratio 35.46, 95%-confidence interval 4.71–519.92) as a significant predictor for DVT. Conclusion DVT is present in a relevant proportion of patients with cerebral ischemia and PFO, which needs to be considered for the individual diagnostic workup. The Wells score seems suitable for guiding additional examinations, i.e., compression ultrasonography.

Published
2024
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11. Deception Abilities Emerged in Large Language Models

Author

Hagendorff, Thilo

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can alter their propensity to deceive. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Published
2023
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12. Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4

Author

Hagendorff, Thilo and Fabi, Sarah

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs, most notably GPT-3, exhibit behavior that strikingly resembles human-like intuition -- and the cognitive errors that come with it. However, LLMs with higher cognitive capabilities, in particular ChatGPT and GPT-4, learned to avoid succumbing to these errors and perform in a hyperrational manner. For our experiments, we probe LLMs with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Moreover, we probe how sturdy the inclination for intuitive-like decision-making is. Our study demonstrates that investigating LLMs with methods from psychology has the potential to reveal otherwise unknown emergent traits., Comment: Overlap with arXiv:2212.05206

Published
2023
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15. Machine Psychology

Author

Hagendorff, Thilo, Dasgupta, Ishita, Binz, Marcel, Chan, Stephanie C. Y., Lampinen, Andrew, Wang, Jane X., Akata, Zeynep, and Schulz, Eric

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning abilities therefore holds significant importance. We argue that a fruitful direction for research is engaging LLMs in behavioral experiments inspired by psychology that have traditionally been aimed at understanding human cognition and behavior. In this article, we highlight and summarize theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks and focuses instead on computational insights that move us toward a better understanding and discovery of emergent abilities and behavioral patterns in LLMs. We review existing work taking this approach, synthesize best practices, and highlight promising future directions. We also highlight the important caveats of applying methodologies designed for understanding humans to machines. We posit that leveraging tools from experimental psychology to study AI will become increasingly valuable as models evolve to be more powerful, opaque, multi-modal, and integrated into complex real-world settings.

Published
2023

16. Methodological reflections for AI alignment research using human feedback

Author

Hagendorff, Thilo and Fabi, Sarah

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

The field of artificial intelligence (AI) alignment aims to investigate whether AI technologies align with human interests and values and function in a safe and ethical manner. AI alignment is particularly relevant for large language models (LLMs), which have the potential to exhibit unintended behavior due to their ability to learn and adapt in ways that are difficult to predict. In this paper, we discuss methodological challenges for the alignment problem specifically in the context of LLMs trained to summarize texts. In particular, we focus on methods for collecting reliable human feedback on summaries to train a reward model which in turn improves the summarization model. We conclude by suggesting specific improvements in the experimental design of alignment studies for LLMs' summarization capabilities.

Published
2022

17. Thinking Fast and Slow in Large Language Models

Author

Hagendorff, Thilo, Fabi, Sarah, and Kosinski, Michal

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs like GPT-3 exhibit behavior that strikingly resembles human-like intuition - and the cognitive errors that come with it. However, LLMs with higher cognitive capabilities, in particular ChatGPT and GPT-4, learned to avoid succumbing to these errors and perform in a hyperrational manner. For our experiments, we probe LLMs with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our study demonstrates that investigating LLMs with methods from psychology has the potential to reveal otherwise unknown emergent traits.

Published
2022
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19. 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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22. Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems

Author

Fabi, Sarah and Hagendorff, Thilo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human cognitive biases in learning algorithms. Secondly, we argue that in order to achieve ethical machine behavior, filter mechanisms have to be applied for selecting biased training stimuli that represent social or behavioral traits that are ethically desirable. We use insights from cognitive science as well as ethics and apply them to the AI field, combining theoretical considerations with seven case studies depicting tangible bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly pursue the idea of a re-evaluation of the ethical significance of machine biases, as well as putting the idea forth to implement cognitive biases into machines.

Published
2022
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23. Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals

Author

Hagendorff, Thilo, Bossert, Leonie, Fai, Tse Yip, and Singer, Peter

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Massive efforts are made to reduce biases in both data and algorithms in order to render AI applications fair. These efforts are propelled by various high-profile cases where biased algorithmic decision-making caused harm to women, people of color, minorities, etc. However, the AI fairness field still succumbs to a blind spot, namely its insensitivity to discrimination against animals. This paper is the first to describe the 'speciesist bias' and investigate it in several different AI systems. Speciesist biases are learned and solidified by AI applications when they are trained on datasets in which speciesist patterns prevail. These patterns can be found in image recognition systems, large language models, and recommender systems. Therefore, AI technologies currently play a significant role in perpetuating and normalizing violence against animals. This can only be changed when AI fairness frameworks widen their scope and include mitigation measures for speciesist biases. This paper addresses the AI community in this regard and stresses the influence AI systems can have on either increasing or reducing the violence that is inflicted on animals, and especially on farmed animals.

Published
2022
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26. The Effect of Upright Posture on Left Atrial Strain in Competitive Athletes

Author

Joscha Kandels, Stephan Stöbe, Robert Percy Marshall, Andreas Hagendorff, and Michael Metze

Subjects
transthoracic echocardiography, athletes, upright posture, deformation, left atrial strain, speckle tracking echocardiography, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background: Left atrial strain (LAS) assessment by speckle tracking echocardiography (STE) has been shown to be a remarkable means of quantifying LA function as an early marker of LV pathology. As exercise testing is also performed on a treadmill, the aim of this study was to investigate the effect of upright posture on LAS in healthy athletes. Methods: Fifty male athletes (mean age 25.7 ± 7.3 years) underwent transthoracic echocardiography (TTE) in the upright and left lateral positions. In addition to the conventional echocardiographic parameters, in all athletes, LA conduction strain (LAScd), contraction strain (LASct), reservoir strain (LASr), and maximum LA volume (LAVmax) were assessed by STE in both positions. Results: Comparing upright posture and the left lateral position, LAScd (−14.0 ± 5.9% vs. −27.4 ± 7.1%; p < 0.001), LASct (−4.6 ± 3.5% vs. −11.3 ± 4.1%; p < 0.001), LASr (18.7 ± 7.6% vs. 38.7 ± 8.0%; p < 0.001), and LAVmax (24.4 ± 8.8% vs. 50.0 ± 14.2%) differed significantly. Conclusions: Upright posture has a significant effect on LA deformation, with decreased LAScd, LASct, and LASr. The results of this study contribute to the understanding of athletes’ hearts and must be considered when performing echocardiography in healthy athletes on a treadmill.

Published
2024
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28. 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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31. Infective endocarditis in adult patients with congenital heart disease

Author

Habib, Gilbert, Lancellotti, Patrizio, Cosyns, Bernard, Donal, Erwan, Erba, Paola, Iung, Bernard, Maggioni, Aldo P., Popescu, Bogdan A., Prendergast, Bernard, Tornos, Pilar, Tatar-Chentir, Nora Nabila Ali, Al-Mallah, Mouaz, Aneq, Meriam Astrom, Athanassopoulos, George, Badano, Luigi Paolo, Benyoussef, Soraya, Aranda, Erick Calderon, Cardim, Nuno Miguel, Chan, Kwan-Leung, Cruz, Ines, Edvardsen, Thor, Goliasch, Georg, Hagendorff, Andreas, Hristova, Krasimira, Kamp, Otto, Kang, Duk-Hyun, Kong, William, Matskeplishvili, Simon, Meshaal, Marwa, Mirocevic, Maja, Neskovic, Aleksandar N., Pazdernik, Michal, Plonska-Gosciniak, Edyta, Raissouni, Maha, Ronderos, Ricardo, Sade, Leyla Elif, Sadeghpour, Anita, Sambola, Antonia, Sengupta, Shantanu, Separovic-Hanzevacki, Jadranka, Takeuchi, Masaaki, Tucay, Edwin, Rodrigues, Ana Clara Tude, Varga, Albert, Vaskelyte, Jolanta, Yamagata, Kentaro, Yiangou, Kyriakos, Zaky, Hosam, Ronderos, R., Avegliano, G., Oses, P. Fernandez, Filipini, E., Granada, I., Iribarren, A., Mahia, M., Nacinovich, F., Ressi, S., Obregon, R., Bangher, M., Dho, J., Cartasegna, L., Plastino, M.L., Novas, V., Shigel, C., Reyes, G., De Santos, M., Gastaldello, N., Fernandez, M. Granillo, Potito, M., Streitenberger, G., Velazco, P., Casabé, J.H., Cortes, C., Guevara, E., Salmo, F., Seijo, M., Weidinger, F., Heger, M., Brooks, R., Stöllberger, C., Ho, C.-Y., Perschy, L., Puskas, L., Goliasch, G., Binder, C., Rosenhek, R., Schneider, M., Winter, M.-P., Hoffer, E., Melissopoulou, M., Lecoq, E., Legrand, D., Jacquet, S., Massoz, M., Lancellotti, P., Pierard, L., Dulgheru, R., Marchetta, S., D'Emal, C., Oury, C., Cosyns, B., Droogmans, S., Kerkhove, D., Motoc, A., Plein, D., Roosens, B., Soens, L., Weytjens, C., Lemoine, I., Rodrigus, I., Paelinck, B., Amsel, B., Unger, P., Konopnicki, D., Beauloye, C., Pasquet, A., Pierard, S., Vancraeynest, D., Vanoverschelde, J.L., Sinnaeve, F., Andrade, J.L., Rodrigues, A.C. Tude, Staszko, K., Monteiro, R. Dos Santos, Miglioranza, M.H., Shuha, D.L., Alcantara, M., Cravo, V., Fazzio, L., Felix, A., Iso, M., Musa, C., Siciliano, A.P., Filho, F. Villaca, Braga, J., Rodrigues, A., Silva, R., Vilela, F., Rodrigues, D., Silva, L., Morhy, S., Fischer, C., Vieira, M., Afonso, T., Abreu, J., Falcao, S.N., Moises, V., Gouvea, A., João, G., Mancuso, F., Silva, C., Souza, A.C., Abboud, C.S., de Mattos Barretto, R. Bellio, Ramos, A., Arnoni, R., Assef, J.E., Togna, D.J. Della, Le Bihan, D., Miglioli, L., Oliveira, A.P. Romero, Kroll, R. Tadeu Magro, Cortez, D., Gelape, C.L., Nunes, M.d.C. Peirira, Ferrari, T.C. De Abreu, Chan, K.-L., Hay, K., Le, V., Page, M., Poulin, F., Sauve, C., Serri, K., Mercure, C., Beaudoin, J., Pibarot, P., Sebag, I., Rudski, L., Ricafort, G., Barsic, B., Krajinovic, V., Vargovic, M., Separovic-Hanzevacki, J., Lovric, D., Reskovic-Luksic, V., Vincelj, J., Jurinjak, S. Jaksic, Yiannikourides, V., Ioannides, M., Kyriakou, C., Pofaides, C., Masoura, V., Yiangou, K., Pudich, J., Linhart, A., Siranec, M., Marek, J., Blechova, K., Kamenik, M., Pazdernik, M., Pelouch, R., Coufal, Z., Mikulica, M., Griva, M., Jancova, E., Mikulcova, M., Taborsky, M., Precek, J., Jecmenova, M., Latal, J., Widimsky, J., Butta, T., Machacek, S., Vancata, R., Spinar, J., Holicka, M., Long, F. Pow Chon, Anzules, N., Carpio, A. Bajana, Largacha, G., Penaherrera, E., Moreira, D., Mahfouz, E., Elsafty, E., Soliman, A., Zayed, Y., Aboulenein, J., Abdel-Hay, M., Almaghraby, A., Abdelnaby, M., Ahmed, M., Hammad, B., Saleh, Y., Zahran, H., Elgebaly, O., Saad, A., Ali, M., Zeid, A., El Sharkawy, R., Meshaal, M., Al Kholy, A., Doss, R., Osama, D., Rizk, H., Elmogy, A., Mishriky, M., Assayag, P., El Hatimi, S., Botelho-Nevers, E., Campisi, S., Fuzellier, J.-F., Gagneux-Brunon, A., Pierrard, R., Tulane, C., Detoc, M., Mehalla, T., Boutoille, D., Al Habash, O., Asseray-Madani, N., Biron, C., Brochard, J., Caillon, J., Cueff, C., Le Tourneau, T., Lecompte, A.S., Lecomte, R., Lefebvre, M., Michel, M.M. Magali, Pattier, S., Delarue, S., Le Bras, M., Orain, J., Faucher, J.-F., Aboyans, V., Beeharry, A., Durox, H., Lacoste, M., Magne, J., Mohty, D., David, A., Pradel, V., Sierra, V., Neykova, A., Bettayeb, B., Elkentaoui, S., Tzvetkov, B., Landry, G., Strady, C., Ainine, K., Baumard, S., Brasselet, C., Tassigny, C., Valente-Pires, V., Lefranc, M., Hoen, B., Lefevre, B., Curlier, E., Callier, C., Fourcade, N., Jobic, Y., Ansard, S., Le Berre, R., Le Roux, P., Le Ven, F., Pouliquen, M.-C., Prat, G., Bouchart, F., Savoure, A., Alarcon, C., Chapuzet, C., Gueit, I., Tribouilloy, C., Bohbot, Y., Peugnet, F., Gun, M., Iung, B., Duval, X., Lescure, X., Ilic-Habensus, E., Sadoul, N., Selton-Suty, C., Alla, F., Chevalier, E., Goehringer, F., Huttin, O., Garcia, R., Le Marcis, V., Tattevin, P., Donal, E., Flecher, E., Revest, M., Habib, G., Hubert, S., Casalta, J.-P., Gouriet, F., Arregle, F., Cammilleri, S., Tessonnier, L., Riberi, A., Chirouze, C., Bouiller, K., Brunel, A.-S., Fournier, D., Hustache-Mathieu, L., Klopfenstein, T., Moreau, J., Lim, P., Oliver, L., Ternacle, J., Moussafeur, A., Chavanet, P., Piroth, L., Buisson, M., Mahy, S., Martins, C., Salmon-Rousseau, A., Gohier, S., Piper, C., Börgermann, J., Guckel, D., Horstkotte, D., Brockmeier, B., Winkelmann, E., Hagendorff, A., Grey, D., Nickenig, G., Schueler, R., Öztürk, C., Stöhr, E., Hamm, C., Walther, T., Brandt, R., Frühauf, A.-C., Hartung, C.T., Hellner, C., Wild, C., Becker, M., Hamada, S., Kaestner, W., Stangl, K., Knebel, F., Baldenhofer, G., Brecht, A., Dreger, H., Isner, C., Pfafflin, F., Stegemann, M., Zahn, R., Fraiture, B., Kilkowski, C., Karcher, A.-K., Klinger, S., Tolksdorf, H., Tousoulis, D., Aggeli, C., Sarri, G., Sideris, S., Venieri, E., Athanassopoulos, G., Tsiapras, D., Armenis, I., Koutsiari, A., Floros, G., Grassos, C., Dragasis, S., Rallidis, L., Varlamos, C., Michalis, L., Naka, K., Bechlioulis, A., Kotsia, A., Lakkas, L., Pappas, K., Papadopoulos, C., Kiokas, S., Lioni, A., Misailidou, S., Barbetseas, J., Bonou, M., Kapelios, C., Tomprou, I., Zerva, K., Manolis, A., Hamodraka, E., Athanasiou, D., Haralambidis, G., Poulimenos, L., Samaras, H., Nagy, A., Bartykowszki, A., Gara, E., Sengupta, S., Mungulmare, K., Kasliwal, R., Bansal, M., Bhan, A., Ranjan, S., Kyavar, M., Maleki, M., Bezanjani, F. Noohi, Sadeghpour, A., Alizadehasl, A., Boudagh, S., Ghavidel, A., Moradnejad, P., Pasha, H.R., Ghadrdoost, B., Gilon, D., Strahilevitz, J., Israel, S., Wanounou, M., d'Agostino, C., Colonna, P., De Michele, L., Fumarola, F., Stante, M., Marchionni, N., Scheggi, V., Alterini, B., Del Pace, S., Stefano, P., Sparano, C., Badano, L.P., Muraru, D., Ruozi, N., Tenaglia, R., Limbruno, U., Cresti, A., Baratta, P., Solari, M., Giannattasio, C., Moreo, A., De Chiara, B., Montero, B. Lopez, Musca, F., Orcese, C.A., Panzeri, F., Russo, C.F., Spano, F., Alfieri, O., De Bonis, M., Agricola, E., Busnardo, E., Carletti, S., Castiglioni, B., Chiappetta, S., Del Forno, B., Ferrara, D., Guffanti, M., Iaci, G., Lapenna, E., Nisi, T., Oltolini, C., Pajoro, U., Pasciuta, R., Ripa, M., Scarpellini, P., Din, C. Tassan, Meneghin, R., Schiavi, D., Piscione, F., Citro, R., Benvenga, R.M., Greco, L., Prota, C., Radano, I., Soriente, L., Bellino, M., Di Vece, D., Santini, F., Salsano, A., Olivieri, G.M., Turrini, F., Messora, R., Tondi, S., Olaru, A., Agnoletto, V., Grassi, L., Leonardi, C., Sansoni, S., Del Ponte, S., Dato, G.M. Actis, De Martino, A., Ohte, N., Kikuchi, S., Wakami, K., Aonuma, K., Seo, Y., Ishizu, T., Machino-Ohtsuka, T., Yamamoto, M., Iida, N., Nakajima, H., Nakagawa, Y., Izumi, C., Amano, M., Miyake, M., Takahashi, K., Shiojima, I., Miyasaka, Y., Maeba, H., Suwa, Y., Taniguchi, N., Tsujimoto, S., Kitai, T., Ota, M., Yuda, S., Sasaki, S., Hagiwara, N., Yamazaki, K., Ashihara, K., Arai, K., Saitou, C., Saitou, S., Suzuki, G., Shibata, Y., Watanabe, N., Nishino, S., Ashikaga, K., Kuriyama, N., Mahara, K., Abe, K., Fujimaki, H., Okubo, T., Shitan, H., Takanashi, S., Terada, M., Yamamoto, H., Sata, M., Yamada, H., Kusunose, K., Saijo, Y., Seno, H., Yuichiro, O., Sakata, Y., Mizuno, H., Nakatani, S., Onishi, T., Sengoku, K., Sera, F., Park, S.W., Kyoung, K. Eun, Yeon, L. Ga, Hwang, J.-W., Jin-Oh, C., Park, S.-J., Sang-Chol, L., Sung-A, C., Jang, S.Y., Kang, D.-H., Heo, R., Lee, S., Song, J.-M., Jung, E., Plisiene, J., Dambrauskaite, A., Gruodyte, G., Jonkaitiene, R., Vaskelyte, J., Mizariene, V., Atkocaityte, J., Zvirblyte, R., Sow, R., Codreanu, A., De la Vega, E.C.L., Michaux, C., Staub, T., Jacobs-Orazi, L., Azzopardi, C. Mallia, Xuereb, R.G., Piscopo, T., Borg, D., Casha, R., Farrugia, J., Fenech, M., Pllaha, E., Vella, C., Yamagata, K., Grib, L., Raevschi, E., Grejdieru, A., Balan, G., Cardaniuc, I., Cardaniuc, L., Corcea, V., Feodorovici, A., Gaina, V., Girbu, L., Jimbei, P., Kravcenco, D., Panfile, E., Prisacari, E., Samohvalov, E., Samohvalov, S., Sceglova, N., Benesco, I., Marian, V., Sumarga, N., Mirocevic, M., Bozovic, B., Bulatovic, N., Lakovic, P., Music, L., Roos-Hesselink, J., Budde, R., Gamela, T., Wahadat, A., Kamp, O., Meijers, T., Van Melle, J.P., Deursen, V.M., Crijns, H., Bekkers, S., Cheriex, E., Gilbers, M., Kietselaer, B., Knackstedt, C., Lorusso, R., Schalla, S., Streukens, S., Chamuleau, S., Cramer, M.-J., Teske, A., Van der Spoel, T., Wind, A., Liesbek, O., Lokhorst, J., Van Heusden, H., Tanis, W., Van der Bilt, I., Vriend, J., Bruggen, H. De Lange-van, Karijodikoro, E., Riezebos, R., van Dongen, E., Schoep, J., Stolk, V., Axler, O., Baumann, F., Lebras, S., Edvardsen, T., Offstad, J.T., Beitnes, J.O., Helle-Valle, T., Skulstad, H., Skardal, R., Qamar, N., Furnaz, S., Ahmed, B., Butt, M.H., Khanzada, M.F., Saghir, T., Wahid, A., Hryniewiecki, T., Szymanski, P., Marzec, K., Misztal-Ogonowska, M., Kosmala, W., Przewlocka-Kosmala, M., Rojek, A., Woznicka, K., Zachwyc, J., Lisowska, A., Kaminska, M., Kasprzak, J., Kowalczyk, E., Strzecka, D.F., Wejner-Mik, P., Trabulo, M., Freitas, P., Ranchordas, S., Rodrigues, G., Pinto, P., Queiros, C., Azevedo, J., Marques, L., Seabra, D., Branco, L., Cruz, M., Galrinho, A., Moreira, R., Rio, P., Timoteo, A.T., Selas, M., Cardim, N.M., Carmelo, V., Neves, B. Duque, Pereira, H., Cruz, I., Guerra, A., Marques, A., Pintassilgo, I., Tomescu, M.C., Trofenciuc, N.-M., Andor, M., Bordejevic, A., Branea, H.S., Caruntu, F., Cirin, L., Citu, I.M., Cotoraci, C.A., Darabantiu, D., Farcas, R., Marincu, I., Mavrea, A., Onel, M.F., Parvanescu, T., Pop, D., Pop-Moldovan, A.L., Puticiu, M.I., Velcean, L.A., Ionac, A., Cozma, D., Mornos, C., Goanta, F., Popescu, I., Beyer, R., Mada, R., Rancea, R., Rosianu, H., Tomoaia, R., Stanescu, C., Kobalava, Z., Karaulova, J., Kotova, E., Milto, A., Pisaryuk, A., Povalyaev, N., Sorokina, M., Alrahimi, J., Elshiekh, A., Jamiel, A., Ahmed, A., Al-Mallah, M., Attia, N., Putnikovic, B., Neskovic, A., Dimic, A., Ivanovic, B., Matic, S., Trifunovic, D., Petrovic, J., Kosevic, D., Dabic, P., Milojevic, P., Petrovic, I., Stojanovic, I., Srdanovic, I., Kovacevic, M., Redzek, A., Stefanovic, M., Susak, S., Velicki, L., Vulin, A., Yeo, T.C., Kong, W.K.F., Poh, K.K., Vilacosta, I., El-Nasser, M. Abd, Ferrera, C., Olmos, C., Iglesias, F. Calvo, Blanco-Gonzalez, E., Amaro, M. Bravo, Germinas, A.N., Lopez-Rodriguez, E., Adan, J. Lugo, Pazos-Lopez, P., Loureiro, M. Pereira, Perez, M.T., Raposeiras-Roubin, S., Yas, S. Rasheed, Suarez-Varela, M.-M., Vidal, F. Vasallo, Garcia-Dorado, D., Sambola, A., Fernandez-Hidalgo, N., Gonzalez-Alujas, T., Lozano, J., Maisterra, O., Pizzi, N., Rios, R., Tornos, P., Bayes-Genis, A., Botet, L. Pedro, Vallejo, N., Berastegui, E., Llibre, C., Mateu, L., Nunez, R., Quesada, D., Portell, D. Bosch, Vinas, J. Aboal, Bertran, X. Albert, Tarradellas, R. Brugada, Ricon, P. Loma-Osorio, de Llano, C. Tiron, Arnau, M.A., Bel, A., Blanes, M., Osa, A., Anguita, M., Carrasco, F., Castillo, J., Zamorano, J.L., Mur, J.L. Moya, Alvaro, M., Fernandez-Golfin, C., Monteagudo, J.M., Elorza, E. Navas, Alvarez, M.C. Farinas, Balbin, J. Aguero, Arminanzas, C., de las Revillas, F. Arnaiz, Garcia, A. Arnaiz, Belaustegui, M. Cobo, Sampedro, M. Fernandez, Cuadra, M. Gutierrez, Gutierrez-Diez, J.F., Zarauza, J., Cuello, L. Garcia, Rico, C. Gonzalez, Rodriguez-Alvarez, R., Goikoetxea, J., Montejo, M., Miro, J., Almela, M., Ambrosioni, J., Falces, C., Fuster, D., Garcia-de-la-Maria, C., Hernandez-Meneses, M., Llopis, J., Marco, F., Moreno, A., Quintana, E., Sandoval, E., Tellez, A., Tolosana, J.M., Vidal, B., Ruiz-Zamora, I., Ruiz, A. Bardaji, Girgas, E. Sanz, Garcia-Pardo, G., Marzo, M. Guillen, Oviedo, A. Rodriguez, Jimenez, A. Villares, Abid, L., Hammami, R., Kammoun, S., Mourali, M.S., Zghal, F. Mghaieth, Hlima, M. Ben, Boudiche, S., Ouali, S., Zakhama, L., Antit, S., Slama, I., Gulel, O., Sahin, M., Sade, L.E., Karacaglar, E., Kucukoglu, S., Cetinarslan, O., Yasar, U.S., Canpolat, U., Mutlu, B., Atas, H., Dervishova, R., Ileri, C., Zaky, H., Alhashmi, J., Baslib, F., Tahir, J., Zarger, P., Woldman, S., Menezes, L., Primus, C., Uppal, R., Bvekerwa, I., Chandrasekaran, B., Kopanska, A., Prendergast, B., Cannata, S., Chambers, J., Hancock, J., Klein, J., Rajani, R., Ursi, M.P., Dworakowski, R., Fife, A., Breeze, J., Browne-Morgan, M., Gunning, M., Streather, S., Asch, F., Zemedkun, M., Alyavi, B., Uzokov, J., van Melle, Joost P., Roos-Hesselink, Jolien W., Bansal, Manish, Pudich, Jiri, Luksic, Vlatka Reskovic, Rodriguez-Alvarez, Regino, Hanzevacki, Jadranka Separovic, Sow, Rouguiatou, Timóteo, Ana Teresa, Morgado, Marisa Trabulo, De Bonis, Michele, Laroche, Cecile, and Boersma, Eric

Published
2023
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33. AI virtues -- The missing link in putting AI ethics into practice

Author

Hagendorff, Thilo

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Several seminal ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, widespread criticism has pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach and the many shortcomings associated with it. This paper proposes a different approach. It defines four basic AI virtues, namely justice, honesty, responsibility and care, all of which represent specific motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two second-order AI virtues, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or the many hidden psychological forces that impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development.

Published
2020
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35. Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study

Author

Kaya, Kenan, Gietzen, Carsten, Hahnfeldt, Robert, Zoubi, Maher, Emrich, Tilman, Halfmann, Moritz C., Sieren, Malte Maria, Elser, Yannic, Krumm, Patrick, Brendel, Jan M., Nikolaou, Konstantin, Haag, Nina, Borggrefe, Jan, Krüchten, Ricarda von, Müller-Peltzer, Katharina, Ehrengut, Constantin, Denecke, Timm, Hagendorff, Andreas, Goertz, Lukas, Gertz, Roman J., Bunck, Alexander Christian, Maintz, David, Persigehl, Thorsten, Lennartz, Simon, Luetkens, Julian A., Jaiswal, Astha, Iuga, Andra Iza, Pennig, Lenhard, and Kottlors, Jonathan

Published
2024
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39. Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study

Author

Kenan Kaya, Carsten Gietzen, Robert Hahnfeldt, Maher Zoubi, Tilman Emrich, Moritz C. Halfmann, Malte Maria Sieren, Yannic Elser, Patrick Krumm, Jan M. Brendel, Konstantin Nikolaou, Nina Haag, Jan Borggrefe, Ricarda von Krüchten, Katharina Müller-Peltzer, Constantin Ehrengut, Timm Denecke, Andreas Hagendorff, Lukas Goertz, Roman J. Gertz, Alexander Christian Bunck, David Maintz, Thorsten Persigehl, Simon Lennartz, Julian A. Luetkens, Astha Jaiswal, Andra Iza Iuga, Lenhard Pennig, and Jonathan Kottlors

Subjects
Cardiovascular magnetic resonance, Generative Pre-trained Transformer 4, Artificial intelligence, Large language models, Myocarditis, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

ABSTRACT: Background: Diagnosing myocarditis relies on multimodal data, including cardiovascular magnetic resonance (CMR), clinical symptoms, and blood values. The correct interpretation and integration of CMR findings require radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis. Methods: This retrospective study includes CMR reports from 396 patients with suspected myocarditis and eight centers, respectively. CMR reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and radiologists with 1 (resident 1), 2 (resident 2), and 4 years (resident 3) of experience in CMR and knowledge of the 2018 Lake Louise Criteria. The final impression of the report regarding the radiological assessment of whether myocarditis is present or not was not provided. The performance of Generative pre-trained transformer 4 (GPT-4) and the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in CMR). Sensitivity, specificity, and accuracy were calculated. Results: GPT-4 yielded an accuracy of 83%, sensitivity of 90%, and specificity of 78%, which was comparable to the physician with 1 year of experience (R1: 86%, 90%, 84%, p = 0.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p = 0.007 and R3: 91%, 85%, 96%, p

Published
2024
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40. Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learning

Author

Hagendorff, Thilo

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia., Comment: 30 pages, 1 figure

Published
2020
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41. Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research

Author

Hagendorff, Thilo and Meding, Kristof

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years - almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation and gender equality. We have obtained four main findings: (1) Academic-corporate collaborations are growing in numbers. At the same time, we found that conflicts of interest are rarely disclosed. (2) Industry publishes papers about trending ML topics on average two years earlier than academia does. (3) Industry papers are not lagging behind academic papers in regard to social impact considerations. (4) Finally, we demonstrate that industrial papers fall short of their academic counterparts with respect to the ratio of gender diversity. We believe that this work is a starting point for an informed debate within and outside of the ML community.

Published
2020
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42. Forbidden knowledge in machine learning -- Reflections on the limits of research and publication

Author

Hagendorff, Thilo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Statistics - Machine Learning
Abstract

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.

Published
2019
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