Ethics of the health-related internet of things: a narrative review

The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT

The ethics of uncertainty for data subjects

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard

AI reflections in 2020

We invited authors of selected Comments and Perspectives published in Nature Machine Intelligence in the latter half of 2019 and first half of 2020 to describe how their topic has developed, what their thoughts are about the challenges of 2020, and what they look forward to in 2021.Postprint (author's final draft

Challenges for Optimizing Real-World Evidence in Alzheimer’s Disease: The ROADMAP Project

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer’s disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit

The ethics of digital well-being: a multidisciplinary perspective

This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being

Enabling posthumous medical data donation: a plea for the ethical utilisation of personal health data

This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, in a way similar to how they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute. Ten arguments are provided to support the need to foster posthumous medical data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to ask for informed consent is rejected, and it instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks providing five foundational principles for ethical medical data donation; and suggesting a draft for such a code

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. Machine learning (ML) approaches - one of the typologies of algorithms underpinning artificial intelligence - are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision making are outlined: a) Risk assessment in the criminal justice system, and b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined

Facilitating Organisational Fluidity with Computational Social Matching

Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Peer reviewe