We propose a principle of sustainability to complement established principles used for justifying healthcare resource allocation. We argue that the application of established principles of equal treatment, need, prognosis and cost-effectiveness gives rise to what we call negative dynamics: a gradual depletion of the value possible to generate through healthcare. These principles should therefore be complemented by a sustainability principle, making the prospect of negative dynamics a further factor to consider, and possibly outweigh considerations highlighted by the other principles. We (...) demonstrate how this principle may take different forms, and show that a commitment to sustainability is supported by considerations internal to the ethical principles already guiding healthcare resource allocation. We also consider two objections. The first of these, we argue, is either based on implausible assumptions or begs the question, whereas the second can be adequately accommodated by the principle we propose. (shrink)

Misconceived Consent: Miguel has stage IV lung cancer. He has nearly exhausted his treatment options when his oncologist, Dr. Llewellyn, tells him about an experimental vaccine trial that may boost his immune response to kill cancer cells. Dr. Llewellyn provides Miguel with a consent form that explains why the study is being conducted, what procedures he will undergo, what the various risks and benefits are, alternative sources of treatment, and so forth. She even sits down with him, carefully talks through (...) the most important points, and gives him time to ask questions. Though it is a Phase 1 study and the chance that he will benefit is very low, Miguel happily agrees to take part. A week later, after the first experimental injection, she asks him if he is worried about the risks. “Risks?” he asks. “I’m sure this is safe—you’re a doctor, after all!”. (shrink)

To give substance to the rhetoric of ‘learning health systems’, a variety of novel trial designs are being explored to more seamlessly integrate research with medical practice, reduce study duration and reduce the number of participants allocated to ineffective interventions. Many of these designs rely on response adaptive randomisation. However, critics charge that RAR is unethical on the grounds that it violates the principle of equipoise. In this paper, I reconstruct critiques of RAR as holding that it is inconsistent with (...) five important ethical principles. I then argue that these criticisms rest on a faulty view of equipoise encouraged by the idea that a RAR study models the beliefs of a single rational agent about the relative merits of the interventions being studied. I outline a view in which RAR models an idealised health system in which diverse communities of fully informed experts shrink or grow as their constituent members update their expert opinions in light of reliable medical evidence. I show how a proper understanding of clinical equipoise can reconcile this conception of RAR with these five ethical principles. This analysis removes an in-principle objection to RAR and sheds important light on the relationship between clinical equipoise and transient diversity in the scientific community. (shrink)

This paper examines the role of equipoise in evaluating international research. It distinguishes two possible formulations of the equipoise requirement that license very different evaluations of international research proposals. The interpretation that adopts a narrow criterion of similarity between clinical contexts has played an important role in one recent controversy, but it suffers from a number of problems. An alternative interpretation that adopts a broader criterion of similarity does a better job of avoiding both exploitation of the brute fact of (...) social deprivation and the exploitation of needy populations for the benefit of more well-off populations. It also holds out the promise of reconciling the need to find interventions that can be employed in developing world contexts with the cluster of moral values that must constrain the way such research is carried out. (shrink)

This paper focuses on the use of ‘black box’ AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI’s implicit assumptions and an individual patient’s background situation.Pacecurrent clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient’s informed consent or violates a more general obligation (...) to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the ‘nature’ and ‘likelihood’ of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasises their significance. (shrink)

In discourses on digitization and the data economy, it is often claimed that data subjects shall beownersof their data. In this paper, we provide a problem diagnosis for such calls fordata ownership: a large variety of demands are discussed under this heading. It thus becomes challenging to specify what—if anything—unites them. We identify four conceptual dimensions of calls for data ownership and argue that these help to systematize and to compare different positions. In view of this pluralism of data ownership (...) claims, we introduce, spell out and defend a constructive interpretative proposal: claims for data ownership are charitably understood as attempts to call for theredistributionof material resources and the socio-culturalrecognitionof data subjects. We argue that as one consequence of this reading, it misses the point to reject claims for data ownership on the grounds that property in data does not exist. Instead, data ownership brings to attention a claim to renegotiate such aspects of thestatus quo. (shrink)

Our decision-making is often subject to framing effects: alternative but equally informative descriptions of the same options elicit different choices. When a decision-maker is vulnerable to framing, she may consent under one description of the act, which suggests that she has waived her right, yet be disposed to dissent under an equally informative description of the act, which suggests that she has not waived her right. I argue that in such a case the decision-maker’s consent is simply irrelevant to the (...) permissibility of proceeding. I then consider two alternative views. According to the first, people susceptible to framing are able to give valid consent so long as they are sufficiently informed. This suggestion, I argue, maintains an overly narrow focus on mere quantity of information to the exclusion of other choice-affecting factors. A second response, which appeals to hypothetical consent, is likewise of little use in resolving the moral problem posed by framing effects. I conclude that if susceptibility to framing undermines the validity of consent, we may have good reason to reconsider whether consent has the rights-waiving function commonly attributed to it. (shrink)

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)

Background Pragmatic randomized controlled trials are designed to evaluate the effectiveness of interventions in real-world clinical conditions. However, these studies raise ethical issues for researchers and regulators. Our objective is to identify a list of key ethical issues in pragmatic RCTs and highlight gaps in the ethics literature. Methods We conducted a scoping review of articles addressing ethical aspects of pragmatic RCTs. After applying the search strategy and eligibility criteria, 36 articles were included and reviewed using content analysis. Results Our (...) review identified four major themes: 1) the research-practice distinction; 2) the need for consent; 3) elements that must be disclosed in the consent process; and 4) appropriate oversight by research ethics committees. 1) Most authors reject the need for a research-practice distinction in pragmatic RCTs. They argue that the distinction rests on the presumptions that research participation offers patients less benefit and greater risk than clinical practice, but neither is true in the case of pragmatic RCTs. 2) Most authors further conclude that pragmatic RCTs may proceed without informed consent or with simplified consent procedures when risks are low and consent is infeasible. 3) Authors who endorse the need for consent assert that information need only be disclosed when research participation poses incremental risks compared to clinical practice. Authors disagree as to whether randomization must be disclosed. 4) Finally, all authors view regulatory oversight as burdensome and a practical impediment to the conduct of pragmatic RCTs, and argue that oversight procedures ought to be streamlined when risks to participants are low. Conclusion The current ethical discussion is framed by the assumption that the function of research oversight is to protect participants from risk. As pragmatic RCTs commonly involve usual care interventions, the risks may be minimal. This leads many to reject the research-practice distinction and question the need for informed consent. But the function of oversight should be understood broadly as protecting the liberty and welfare interest of participants and promoting public trust in research. This understanding, we suggest, will focus discussion on questions about appropriate ethical review for pragmatic RCTs. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

Pragmatic randomized controlled trials are designed to evaluate the effectiveness of interventions in real-world clinical conditions. However, these studies raise ethical issues for researchers and regulators. Our objective is to identify a list of key ethical issues in pragmatic RCTs and highlight gaps in the ethics literature. We conducted a scoping review of articles addressing ethical aspects of pragmatic RCTs. After applying the search strategy and eligibility criteria, 36 articles were included and reviewed using content analysis. Our review identified four (...) major themes: 1) the research-practice distinction; 2) the need for consent; 3) elements that must be disclosed in the consent process; and 4) appropriate oversight by research ethics committees. 1) Most authors reject the need for a research-practice distinction in pragmatic RCTs. They argue that the distinction rests on the presumptions that research participation offers patients less benefit and greater risk than clinical practice, but neither is true in the case of pragmatic RCTs. 2) Most authors further conclude that pragmatic RCTs may proceed without informed consent or with simplified consent procedures when risks are low and consent is infeasible. 3) Authors who endorse the need for consent assert that information need only be disclosed when research participation poses incremental risks compared to clinical practice. Authors disagree as to whether randomization must be disclosed. 4) Finally, all authors view regulatory oversight as burdensome and a practical impediment to the conduct of pragmatic RCTs, and argue that oversight procedures ought to be streamlined when risks to participants are low. The current ethical discussion is framed by the assumption that the function of research oversight is to protect participants from risk. As pragmatic RCTs commonly involve usual care interventions, the risks may be minimal. This leads many to reject the research-practice distinction and question the need for informed consent. But the function of oversight should be understood broadly as protecting the liberty and welfare interest of participants and promoting public trust in research. This understanding, we suggest, will focus discussion on questions about appropriate ethical review for pragmatic RCTs. (shrink)

Although informed consent is important in clinical research, questions persist regarding when it is necessary, what it requires, and how it should be obtained. The standard view in research ethics is that the function of informed consent is to respect individual autonomy. However, consent processes are multidimensional and serve other ethical functions as well. These functions deserve particular attention when barriers to consent exist. We argue that consent serves seven ethically important and conceptually distinct functions. The first four functions pertain (...) principally to individual participants: (1) providing transparency; (2) allowing control and authorization; (3) promoting concordance with participants' values; and (4) protecting and promoting welfare interests. Three other functions are systemic or policy focused: (5) promoting trust; (6) satisfying regulatory requirements; and (7) promoting integrity in research. Reframing consent around these functions can guide approaches to consent that are context sensitive and that maximize achievable goals. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...

Making good decisions in extremely complex and difficult processes and situations has always been both a key task as well as a challenge in the clinic and has led to a large amount of clinical, legal and ethical routines, protocols and reflections in order to guarantee fair, participatory and up-to-date pathways for clinical decision-making. Nevertheless, the complexity of processes and physical phenomena, time as well as economic constraints and not least further endeavours as well as achievements in medicine and healthcare (...) continuously raise the need to evaluate and to improve clinical decision-making. This article scrutinises if and how clinical decision-making processes are challenged by the rise of so-called artificial intelligence-driven decision support systems (AI-DSS). In a first step, this article analyses how the rise of AI-DSS will affect and transform the modes of interaction between different agents in the clinic. In a second step, we point out how these changing modes of interaction also imply shifts in the conditions of trustworthiness, epistemic challenges regarding transparency, the underlying normative concepts of agency and its embedding into concrete contexts of deployment and, finally, the consequences for (possible) ascriptions of responsibility. Third, we draw first conclusions for further steps regarding a ‘meaningful human control’ of clinical AI-DSS. (shrink)

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)