Suchergebnisse

Intro -- Danksagung -- Vorwort zur 5. Auflage -- Inhaltsverzeichnis -- Über die Autoren -- 1 Entwicklung der Bauwerksabdichtung -- 2 Denkmalpflegerische Betrachtungen und Denkmalpflegerische Aspekte in der Bauwerksabdichtung/Bauwerkssanierung -- 3 Grundlagen in der Bauwerksabdichtung -- 4 Geotechnische Grundlagen -- 5 Bestandsanalyse und Mauerwerksdiagnostik -- 6 Messgeräte und -verfahren in der Bauwerksabdichtung -- 7 Abdichtungskonzept und Wirksamkeitsnachweis in der Baupraxis -- 8 Bauteiltrocknung in der Altbausanierung -- 9 Nachträgliche mechanische Horizontalsperre -- 10 Horizontalsperren im Injektionsverfahren -- 11 Nachträgliche Vertikalabdichtung -- 12 Abdichtungen im Sockelbereich -- 13 Abdichtungen von Balkonen, Terrassen und Laubengängen -- 14 Flächenabdichtung durch Injektion -- 15 Schleierinjektion -- 16 Abdichtungen mit Betonkonstruktionen -- 17 Abdichtungen im Innenbereich -- 18 Physikalische Verfahren und elektrophysikalische Verfahren -- 19 Flankierende Maßnahmen -- 20 Qualitätssicherung -- 21 Grundlagen der Beweissicherung -- 22 Zivilrechtliche Grundlagen -- 23 WTA-Merkblatt 4-7: Nachträgliche mechanische Horizontalsperre -- 24 WTA-Merkblatt 4-10: Injektionsverfahren mit zertifizierten Injektionsstoffen gegen kapillaren Feuchtetransport -- Sachverzeichnis.

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.

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.