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

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

Fair retail banking : how to prevent mis-selling by banks

Mis-selling by banks has occurred repeatedly in many nations over the last decade. While clients may benefit from competition – enabling them to choose financial services at lowest costs – economic frictions between banks and clients may give rise to mis-selling. Examples of mis-selling are mis-representation of information, overly complex product design and non-customized advice. European regulators address the problem of mis-selling in the "Markets in Financial Instruments Directive" (MiFID) I and II and the "Markets in Financial Instruments Regulation" (MiFIR), by setting behavioral requirements for banks, regulating the compensation of employees, and imposing re-quirements on offered financial products and disclosure rules. This paper argues that MiFID II protects clients but is not as effective as it could be. (1) It does not differentiate between client groups with different levels of financial literacy. Effective advice requires different advice for different client groups. (2) MiFID II uses too many rules and too many instruments to achieve identical goals and thereby generates excessive compliance costs. High compliance costs and low revenues would drive banks out of some segments of retail business.publishe

Fair retail banking: how to prevent mis-selling by banks

Mis-selling by banks has occurred repeatedly in many nations over the last decade. While clients may benefit from competition – enabling them to choose financial services at lowest costs – economic frictions between banks and clients may give rise to mis-selling. Examples of mis-selling are mis-representation of information, overly complex product design and non-customized advice. European regulators address the problem of mis-selling in the "Markets in Financial Instruments Directive" (MiFID) I and II and the "Markets in Financial Instruments Regulation" (MiFIR), by setting behavioral requirements for banks, regulating the compensation of employees, and imposing re-quirements on offered financial products and disclosure rules. This paper argues that MiFID II protects clients but is not as effective as it could be. (1) It does not differentiate between client groups with different levels of financial literacy. Effective advice requires different advice for different client groups. (2) MiFID II uses too many rules and too many instruments to achieve identical goals and thereby generates excessive compliance costs. High compliance costs and low revenues would drive banks out of some segments of retail business

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