In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

The role of scientists as experts is crucial to public policymaking. However, the expert role is contested and unsettled in both public and scholarly discourse. In this paper, I provide a systematic account of the role of scientists as experts in policymaking by examining whether there are any normatively relevant differences between this role and the role of scientists as researchers. Two different interpretations can be given of how the two roles relate to each other. The separability view states that (...) there is a normatively relevant difference between the two roles, whereas the inseparability view denies that there is such a difference. Based on a systematic analysis of the central aspects of the role of scientists as experts – that is, its aim, context, mode of output, and standards, I propose a moderate version of the separability view. Whereas the aim of scientific research is typically to produce new knowledge through the use of scientific method for evaluation and dissemination in internal settings, the aim of the expert is to provide policymakers and the public with relevant and applicable knowledge that can premise political reasoning and deliberation. (shrink)

The combination of this vindication of moral deference and diagnosis of its fishiness nicely accommodates, I argue, some related phenomena, like the (neglected) fact that our uneasiness with moral deference is actually a particular instance of uneasiness with opaque evidence in general when it comes to morality, and the (familiar) fact that the scope of this uneasiness is wider than the moral as it includes other normative domains.

Over the course of its first seven editions, Principles of Biomedical Ethics has proved to be, globally, the most widely used, authored work in biomedical ethics. It is unique in being a book in bioethics used in numerous disciplines for purposes of instruction in bioethics. Its framework of moral principles is authoritative for many professional associations and biomedical institutions-for instruction in both clinical ethics and research ethics. It has been widely used in several disciplines for purposes of teaching in the (...) classroom, during conferences, etc. (shrink)

Every year in this country, some 10,000 college and university courses are taught in applied ethics. And many professional organizations now have their own codes of ethics. Yet social science has had little impact upon applied ethics. This book promises to change that trend by illustrating how social science can make a contribution to applied ethics. The text reports psychological studies relevant to applied ethics for many professionals, including accountants, college students and teachers, counselors, dentists, doctors, journalists, nurses, school teachers, (...) athletes, and veterinarians. Each chapter begins with the research base of the cognitive-developmental approach--especially linked to Kohlberg and Rest's Defining Issues Test. Finally, the book summarizes recent research on the following issues: * moral judgment scores within and between professions, * pre- and post-test evaluations of ethics education programs, * moral judgment and moral behavior, * models of professional ethicseducation, and * models for developing new assessment tools. Researchers in different professional fields investigate different questions, develop different research strategies, and report different findings. Typically researchers of one professional field are not aware of research in other fields. An important aim of the present book is to bring this diverse research together so that cross-fertilization can occur and ideas from one field can transfer to another. (shrink)

Artificial intelligence is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates (...) both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making. (shrink)

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied (...) in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making. (shrink)