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This book explores how one can bring about changes in the brain through meditation, both through attention-focus training and through compassion training. Recent findings in the natural sciences have confirmed that it is possible for humans to achieve these structural and functional changes through various life-style practices. It is argued that meditation enables us to influence some aspects of our biological make-up and, for example, could boost our cognitive flexibility as well as our ability to act compassionate. Such changes are likely to facilitate the instilling of a number of epistemic virtues which have great bearing on our quality of life. This book offers the reader an accessible introduction to a set of neuro-enhancement methods, with a special focus on meditation techniques, and explores how such practices could contribute to make us better decision-makers and improve our moral virtues. The book is suitable for anyone looking for a text discussing the effects of neuro-enhancement from a secular ethics perspective

This document contains a conceptual framework for the classification and impact assessment of policy measures adopted in Europe since the beginning of the COVID-19 pandemic. We develop a framework that will guide all partners in PERISCOPE in their research endeavours, with the goal of evaluating which measures proved to be most effective since the beginning of the pandemic. PERISCOPE aims at collecting data on impacts, as well as on the policy measures and governance choices adopted by policymakers at all levels of government. We thereby adopt a taxonomy of impacts and a taxonomy of policy measures. In laying the foundations for our assessment, we adopt a theoretical framework that goes beyond the notion of economic growth and GDP, as well as beyond the cost-benefit analysis of policies, to embrace a framework based on three interrelated concepts: subjective well-being, resilience, and sustainability.

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.