What exactly is a “wicked problem”? It is a social or economic problem that is so complex and so interconnected with other issues that it is extraordinarily difficult or impossible to resolve. This is because all proposed resolutions generate equally complex, equally wicked problems. In this essay, I argue that precision medicine, especially in the context of the U.S. healthcare system, generates numerous wicked problems related to distributive justice. Further, I argue that there are no easy solutions to these wicked problems. The need for trade-offs is inescapable. Rough justice is the best outcome we can hope for, and that outcome requires a commitment to processes of public reason that are fair and inclusive.

The question that this paper tries to answer is Q: “Can good academic bioethics be done without commitment to moral theory?” It is argued that the answer to Q is an unequivocal “Yes” for most of what we could call “critical bioethics,” that is, the kind of bioethics work that primarily criticizes positions or arguments already in the literature or put forward by policymakers. The answer is also “Yes” for much of empirical bioethics. The second part of the paper then provides an analysis of Q in relation to “constructive bioethics,” that is, bioethics work aimed at providing an argument for a particular position. In this part, it is argued that a number of the approaches or methods used that initially look like they involve no commitment to moral theory, nevertheless, involve such a commitment. This is shown to be the case for reflective equilibrium, mid-level theory, the use of theory fragments, and argument by analogy.

Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI’s extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin’s accountability for reasonableness conception, needs amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create.

Recent global events demonstrate that analytical frameworks to aid professionals in healthcare ethics must consider the pervasive role of social structures in the emergence of bioethical issues. To address this, the authors propose a new sociologically informed approach to healthcare ethics that they term “social bioethics.” Their approach is animated by the interpretive social sciences to highlight how social structures operate vis-à-vis the everyday practices and moral reasoning of individuals, a phenomenon known as social discourse. As an exemplar, the authors use social bioethics to reframe common ethical issues in psychiatric services and discuss potential implications. Lastly, the authors discuss how social bioethics illuminates the ways healthcare ethics consultants in both policy and clinical decision-making participate in and shape broader social, political, and economic systems, which then cyclically informs the design and delivery of healthcare.

This article highlights the continuing relevance of a classic bioethical text, “Bioethics as a Discipline,” published by the Hastings Center’s cofounder Daniel Callahan in 1973. Connecting the text’s programmatic recommendations with later reflections and interventions Callahan wrote about the development of bioethics illuminates how the vision Callahan established and the reality this vision helped create were interrelated—just not in the way Callahan had hoped for. Although this portrait relies on an individual perception of the development of bioethics, it might nevertheless, through its unique linkage of different bioethical temporalities, contribute to a broader reassessment of what bioethics became and why.

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are “black boxes.” The initial response in the literature was a demand for “explainable AI.” However, recently, several authors have suggested that making AI more explainable or “interpretable” is likely to be at the cost of the accuracy of these systems and that prioritizing interpretability in medical AI may constitute a “lethal prejudice.” In this paper, we defend the value of interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realized. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems, may itself constitute a form of lethal prejudice that may diminish the benefits of AI to—and perhaps even harm—patients.

One of the criteria used by many transplant centers in assessing psychosocial eligibility for solid organ transplantation is social support. Yet, social support is a highly controversial requirement that has generated ongoing debate between ethicists and clinicians who favor its consideration (i.e., utility maximizers) and those who object to its use on equity grounds (i.e., equity maximizers). The assumption underlying both of these approaches is that social support is not a commodity that can be purchased in the marketplace. This essay argues for the reconceptualization of social support as a product that can—and should—be purchased for transplant candidates to render them eligible for transplant.

Pragmatism gained considerable attention in bioethical discussions in the early 21st century. However, some dimensions and contributions of pragmatism to bioethics remain underexplored in both research and practice. It is argued that pragmatism can make a distinctive contribution to bioethics through its concept, developed by Charles S. Peirce and John Dewey, that ethical issues can be resolved through experimental inquiry. Dewey’s proposal that policies can be confirmed or disconfirmed through experimentation is developed by comparing it to the confirmation of scientific hypotheses, with a focus on the objection that the consequences of following a moral view or policy do not provide guidance on choosing among competing ethical perspectives. As confirmation of scientific hypotheses typically relies on evidence gathered from observation, the possibility of ethically relevant observation is then explored based on Peirce’s views on feelings as emotional interpretants. Finally, the connection between Dewey’s experimental ethics and democracy is outlined and compared to unfettered ethical progressivism.

Luck egalitarianism is a responsibility-sensitive theory of distributive justice. Its application to health and healthcare is controversial. This article addresses a novel critique of luck egalitarianism, namely, that it wrongfully discriminates against those responsible for their health disadvantage when allocating scarce healthcare resources. The philosophical literature about discrimination offers two primary reasons for what makes discrimination wrong (when it is): harm and disrespect. These two approaches are employed to analyze whether luck egalitarian healthcare prioritization should be considered wrongful discrimination. Regarding harm, it is very plausible to consider the policies harmful but much less reasonable to consider those responsible for their health disadvantages a socially salient group. Drawing on the disrespect literature, where social salience is typically not required for something to be discrimination, the policies are a form of discrimination. They are, however, not disrespectful. The upshot of this first assessment of the discrimination objection to luck egalitarianism in health is, thus, that it fails.

Position papers on artificial intelligence (AI) ethics are often framed as attempts to work out technical and regulatory strategies for attaining what is commonly called trustworthy AI. In such papers, the technical and regulatory strategies are frequently analyzed in detail, but the concept of trustworthy AI is not. As a result, it remains unclear. This paper lays out a variety of possible interpretations of the concept and concludes that none of them is appropriate. The central problem is that, by framing the ethics of AI in terms of trustworthiness, we reinforce unjustified anthropocentric assumptions that stand in the way of clear analysis. Furthermore, even if we insist on a purely epistemic interpretation of the concept, according to which trustworthiness just means measurable reliability, it turns out that the analysis will, nevertheless, suffer from a subtle form of anthropocentrism. The paper goes on to develop the concept of strange error, which serves both to sharpen the initial diagnosis of the inadequacy of trustworthy AI and to articulate the novel epistemological situation created by the use of AI. The paper concludes with a discussion of how strange error puts pressure on standard practices of assessing moral culpability, particularly in the context of medicine.

The paper aims to present a critical analysis of the phenomenon and notion of exceptionalism in bioethics. The authors demonstrate that exceptionalism pertains to phenomena that are not (yet) entirely familiar to us and could potentially bear risks regarding their regulation. After an overview of the state of the art, we briefly describe the origins and evolution of the concept, compared to exception and exclusion. In the second step, they look at the overall development debates on genetic exceptionalism, compared to other bioethical debates on exceptionalism, before presenting a detailed analysis of a specific case of early regulation of genetic screening. In the last part, the authors explain the historical background behind the connection between exceptionalism and exclusion in these debates. Their main conclusion is that while the initial stage of the discussion is shaped by the concept of exceptionalism and awareness of risks of exclusion, the later development centers around exceptions that are needed in detailed regulatory procedures.

It is a common view that artificial systems could play an important role in dealing with the shortage of caregivers due to demographic change. One argument to show that this is also in the interest of care-dependent persons is that artificial systems might significantly enhance user autonomy since they might stay longer in their homes. This argument presupposes that the artificial systems in question do not require permanent supervision and control by human caregivers. For this reason, they need the capacity for some degree of moral decision-making and agency to cope with morally relevant situations (artificial morality). Machine ethics provides the theoretical and ethical framework for artificial morality. This article scrutinizes the question how artificial moral agents that enhance user autonomy could look like. It discusses, in particular, the suggestion that they should be designed as moral avatars of their users to enhance user autonomy in a substantial sense.

Capacity evaluation has become a widely used assessment device in clinical practice to determine whether patients have the cognitive ability to render their own medical decisions. Such evaluations, which might be better thought of as “capacity challenges,” are generally thought of as benign tools used to facilitate care. This paper proposes that such challenges should be reconceptualized as significant medical interventions with their own set of risks, side effects, and potentially deleterious consequences. As a result, a cost–benefit analysis should be implemented prior to imposing such capacity challenges, and efforts should be made to minimize such challenges in situations where they are unlikely to alter the course of treatment.

Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust in nonhuman agents constitutes a category error and worry about the concept being misused for ethics washing. Proponents of trust have responded to these worries from various angles, disentangling different concepts and aspects of trust in AI, potentially organized in layers or dimensions. Given the substantial disagreements across these accounts of trust and the important worries about ethics washing, we embrace a diverging strategy here. Instead of aiming for a positive definition of the elements and nature of trust in AI, we proceed ex negativo, that is we look at cases where trust or distrust are misplaced. Comparing these instances with trust expedited in doctor–patient relationships, we systematize these instances and propose a taxonomy of both misplaced trust and distrust. By inverting the perspective and focusing on negative examples, we develop an account that provides useful ethical constraints for decisions in clinical as well as regulatory contexts and that highlights how we should not engage with medical AI.

A number of countries and states prohibit surrogacy except in cases of “medical necessity” or for those with specific medical conditions. Healthcare providers in some countries have similar policies restricting the provision of clinical assistance in surrogacy. This paper argues that surrogacy is never medically necessary in any ordinary understanding of this term. The author aims to show first that surrogacy per se is a socio-legal intervention and not a medical one and, second, that the intervention in question does not treat, prevent, or mitigate any actual or potential harm to health. Legal regulations and healthcare-provider policies of this kind therefore codify a fiction—one which both obscures the socio-legal motivations for surrogacy and inhibits critical examination of those motivations while mobilizing normative connotations of appeals to medical need. The persisting distinction, in law and in moral discourse, between “social” and “medical” surrogacy, is unjustified.

The leading models of disability struggle to fully encompass all aspects of “disability.” This difficulty arises, the author argues, because the models fundamentally misunderstand the nature of disability. Current theoretical approaches to disability can be understood as “nounal,” in that they understand disability as a thing that is caused or embodied. In contrast, this paper presents an adverbial perspective on disability, which shows that disability is experienced as a personally irremediable impediment to daily-living tasks or goals-like-ours. The picture theory of disability technically constitutes a species of relational approach because its analysis references the interplay between an individual and their environment; it differs from other relational accounts, however, by interpreting disability as a certain kind of negative experience—rather than a function of that relationship. This purely descriptive theory makes no normative claims about disability and operates as both a mechanism for the evaluation of the experience of disability and a heuristic device for the proper interpretation of disability. When disability is reframed in this way, the theory offers a particularist perspective which shows if, when, where, and how disability is experienced.

This article presents a revised version of negative utilitarianism. Previous versions have relied on a hedonistic theory of value and stated that suffering should be minimized. The traditional rebuttal is that the doctrine in this form morally requires us to end all sentient life. To avoid this, a need-based theory of value is introduced. The frustration of the needs not to suffer and not to have one’s autonomy dwarfed should, prima facie, be decreased. When decreasing the need frustration of some would increase the need frustration of others, the case is deferred and a fuller ethical analysis is conducted. The author’s perceptions on murder, extinction, the right to die, antinatalism, veganism, and abortion are used to reach a reflective equilibrium. The new theory is then applied to consumerism, material growth, and power relations. The main finding is that the burden of proof should be on those who promote the status quo.

I present a qualified new defense of antinatalism. It is intended to empower potential parents who worry about their possible children’s life quality in a world threatened by environmental degradation, climate change, and the like. The main elements of the defense are an understanding of antinatalism’s historical nature and contemporary varieties, a positional theory of value based on Epicurean hedonism and Schopenhauerian pessimism, and a sensitive guide for reproductive decision-making in the light of different views on life’s value and risk-taking. My conclusion, main message, to the concerned would-be parents is threefold. If they believe that life’s ordinary frustrations can make it not worth living, they should not have children. If they believe that a noticeably low life quality makes it not worth living and that such life quality can be reasonably expected, they should not have children, either. If they believe that a noticeably low life quality is not reasonably to be expected or that the risk is worth taking, they can, in the light of their own values and beliefs, have children. The conclusion is supported by a combination of the extant arguments for reproductive abstinence, namely the arguments from consent, moral asymmetry, life quality, and risk.