Back to Eudaimonia as a Social Relation: What Does the Covid Crisis Teach Us about Individualism and its Limits?

The current health crisis that has spread worldwide has raised many questions regarding our relations to the Other and to ourselves. Through isolating people, Covid-19 has demonstrated the need we face, as human beings, to socialize and to get in contact, physically speaking, with others. As Aristotle stated, human beings are political animals, meaning social animals that can flourish only in the polis through the process of interacting with each other in quest of eudaimonia, i.e. happiness. Along with the rise of socio-physical distancing imposed due to the pandemic, people around the world have experienced isolation and the lack of human contact and interaction. In the Western world this isolation has led to an increase in mental health issues, and this fact has to be taken into consideration by the government when making decisions regarding the reinforcement or the slackening of measures in the context of Covid. The pandemic has shed a light on the limits of individualism as it has developed in some places. The quest for happiness has slowly led some societies to create a kind of a solipsistic world in which there would exist no reality, no truth outside individuals’ perceptions. Consequently, each human being is considered as “the measure of all things,” as Protagoras noted. This unique experience could then give us the grounds to question our relations to each other, to investigate our understanding of eudaimonia, and to revisit what it means to live in a society

Back to Eudaimonia as a Social Relation: What Does the Covid Crisis Teach Us about Individualism and its Limits?

The current health crisis that has spread worldwide has raised many questions regarding our relations to the Other and to ourselves. Through isolating people, Covid-19 has demonstrated the need we face, as human beings, to socialize and to get in contact, physically speaking, with others. As Aristotle stated, human beings are political animals, meaning social animals that can flourish only in the polis through the process of interacting with each other in quest of eudaimonia, i.e. happiness. Along with the rise of socio-physical distancing imposed due to the pandemic, people around the world have experienced isolation and the lack of human contact and interaction. In the Western world this isolation has led to an increase in mental health issues, and this fact has to be taken into consideration by the government when making decisions regarding the reinforcement or the slackening of measures in the context of Covid. The pandemic has shed a light on the limits of individualism as it has developed in some places. The quest for happiness has slowly led some societies to create a kind of a solipsistic world in which there would exist no reality, no truth outside individuals’ perceptions. Consequently, each human being is considered as “the measure of all things,” as Protagoras noted. This unique experience could then give us the grounds to question our relations to each other, to investigate our understanding of eudaimonia, and to revisit what it means to live in a society

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

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.</jats:p

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

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.</jats:p

L’institutionnalisation des normes éthiques dans le domaine de l’intelligence artificielle: une construction sociale à dépasser

Cet article ouvre la réflexion sur le poids de la norme dans la compétition internationale autour de l’éthique appliquée l’intelligence artificielle. En utilisant le cas de l’Union européenne et en s’appuyant sur le constructivisme social, il vise à démontrer que le discours cosm-éthique développé par l’UE contribue à la création des normes entourant l’IA et façonnent les perceptions pour in fine faciliter l’acceptation de ces normes

Co-design of a trustworthy AI system in healthcare : deep learning based skin lesion classifier

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

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

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