Search

Your search keyword '"Kringen, Pedro"' showing total 25 results
Start Over Author "Kringen, Pedro" Database OAIster
25 results on '"Kringen, Pedro"'

1. 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, Zicari, Roberto V., 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.

Abstract

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.

Published
2023

2. 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, Zicari, Roberto V., 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.

Abstract

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.

Published
2023

3. How to Assess Trustworthy AI in Practice

Author

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

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.

Published
2022

4. How to Assess Trustworthy AI in Practice

Author

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

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.

Published
2022

5. 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, Wurth, Renee, 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

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
Full Text
View/download PDF

6. On assessing trustworthy AI in healthcare : 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, Kararigas, Georgios, 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

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
Full Text
View/download PDF

7. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021
Full Text
View/download PDF

8. On assessing trustworthy AI in healthcare : 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, Kararigas, Georgios, 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

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
Full Text
View/download PDF

9. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021
Full Text
View/download PDF

10. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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

Published
2021

11. On Assessing Trustworthy AI in Healthcare: 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, Kararigas, Georgios, 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

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

12. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021
Full Text
View/download PDF

13. On assessing trustworthy AI in healthcare : 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, Kararigas, Georgios, 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

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
Full Text
View/download PDF

14. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021
Full Text
View/download PDF

15. On assessing trustworthy AI in healthcare : 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, Kararigas, Georgios, 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

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
Full Text
View/download PDF

16. On assessing trustworthy AI in healthcare. 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 Istvan, 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, Kararigas, Georgios, 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 Istvan, 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

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

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

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.

Published
2021

18. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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

Published
2021

19. On Assessing Trustworthy AI in Healthcare: 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, Kararigas, Georgios, 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

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

20. Z-Inspection®: A Process to Assess Trustworthy AI

Author

Zicari, Roberto V., Brodersen, John, Brusseau, James, Dudder, Boris, Eichhorn, Timo, Ivanov, Todor, Kararigas, Georgios, Kringen, Pedro, McCullough, Melissa, Moslein, Florian, Mushtaq, Naveed, Roig, Gemma, Sturtz, Norman, Tolle, Karsten, Tithi, Jesmin Jahan, Halem, Irmhild van, Westerlund, Magnus, Zicari, Roberto V., Brodersen, John, Brusseau, James, Dudder, Boris, Eichhorn, Timo, Ivanov, Todor, Kararigas, Georgios, Kringen, Pedro, McCullough, Melissa, Moslein, Florian, Mushtaq, Naveed, Roig, Gemma, Sturtz, Norman, Tolle, Karsten, Tithi, Jesmin Jahan, Halem, Irmhild van, and Westerlund, Magnus

Published
2021

21. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021

22. 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, Kararigas, Georgios, 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

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

23. Z-Inspection®: A Process to Assess Trustworthy AI

Author

Zicari, Roberto V., Brodersen, John, Brusseau, James, Dudder, Boris, Eichhorn, Timo, Ivanov, Todor, Kararigas, Georgios, Kringen, Pedro, McCullough, Melissa, Moslein, Florian, Mushtaq, Naveed, Roig, Gemma, Sturtz, Norman, Tolle, Karsten, Tithi, Jesmin Jahan, Halem, Irmhild van, Westerlund, Magnus, Zicari, Roberto V., Brodersen, John, Brusseau, James, Dudder, Boris, Eichhorn, Timo, Ivanov, Todor, Kararigas, Georgios, Kringen, Pedro, McCullough, Melissa, Moslein, Florian, Mushtaq, Naveed, Roig, Gemma, Sturtz, Norman, Tolle, Karsten, Tithi, Jesmin Jahan, Halem, Irmhild van, and Westerlund, Magnus

Published
2021

24. 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, Kararigas, Georgios, 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

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

25. 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 Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, Wurth, Renee, Zicari, Roberto V., Ahmed, Sheraz, Amann, Julia, Braun, Stephan Alexander, Brodersen, John, Bruneault, Frédérick, Brusseau, James, Campano, Erik, Coffee, Megan, Dengel, Andreas, Düdder, Boris, Gallucci, Alessio, Gilbert, Thomas Krendl, Gottfrois, Philippe, Goffi, Emmanuel, Haase, Christoffer Bjerre, 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 Jahan, Umbrello, Steven, Vetter, Dennis, Volland, Holger, Westerlund, Magnus, and Wurth, Renee

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.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results