What proper role should considerations of risk, particularly to research subjects, play when it comes to conducting research on human enhancement in the military context? We introduce the currently visible military enhancement techniques (1) and the standard discussion of risk for these (2), in particular what we refer to as the ‘Assumption’, which states that the demands for risk-avoidance are higher for enhancement than for therapy. We challenge the Assumption through the introduction of three categories of enhancements (3): therapeutic, preventive, (...) and pure enhancements. This demands a revision of the Assumption (4), alongside which we propose some further general principles bearing on how to balance risks and benefits in the context of military enhancement research. We identify a particular type of therapeutic enhancements as providing a more responsible path to human trials of the relevant interventions than pure enhancement applications. Finally, we discuss some possible objections to our line of thought (5). While acknowledging their potential insights, we ultimately find them to be unpersuasive, at least provided that our proposal is understood as fully non-coercive towards the candidates for such therapeutic enhancement trials. (shrink)

This book sheds light on various ethical challenges military and humanitarian health care personnel face while working in adverse conditions. Contexts of armed conflict, hybrid wars or other forms of violence short of war, as well as natural disasters, all have in common that ordinary circumstances can no longer be taken for granted. Hence, the provision of health care has to adapt, for example, to a different level of risk, to scarce resources, or uncommon approaches due to external incentives or (...) requirements. This affects the practice of health care as well as its ethics. This book offers a panoramic overview on various challenges healthcare faces in extraordinary situations and provides new insights from practitioners’ as well as from academic scholars’ perspectives. (shrink)

Patient preference predictors (PPPs) promise to provide medical professionals with a new solution to the problem of making treatment decisions on behalf of incapacitated patients. I show that the use of PPPs faces a version of a normative problem familiar from legal scholarship: the problem of naked statistical evidence. I sketch two sorts of possible reply, vindicating and debunking, and suggest that our reply to the problem in the one domain ought to mirror our reply in the other. The conclusion (...) is thus conditional: if we think the problem of naked statistical evidence is a serious problem in the legal domain, then we should be concerned about the symmetrical problem for PPPs. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

End-of-life decision making constitutes a major challenge for bioethical deliberation and political governance in modern democracies: On the one hand, it touches upon fundamental convictions about life, death, and the human condition. On the other, it is deeply rooted in religious traditions and historical experiences and thus shows great socio-cultural diversity. The bioethical discussion of such cultural issues oscillates between liberal individualism and cultural stereotyping. Our paper confronts the bioethical expert discourse with public moral attitudes. The paper is based on (...) a qualitative study comprising 12 focus group discussions with religious and secular persons in the USA, Germany, and Israel. Considering the respective socio-political and legal frameworks, the thematic analysis focuses on moral attitudes towards end-of-life decision making and explores the complex interplay between individual preferences, culture, and religion. Our findings draw attention to the variety and complexity of cultural and religious aspects of end-of-life decision making. Although there is local consensus that goes beyond radical individualism, positions are not neatly matched with national cultures or religious denominations. Instead, the relevance of the specific situatedness of religious beliefs and cultural communities becomes visible: Their status and role in individual situations, for example, as consensual or conflicting on the level of personal perspectives, family relationships, or broader social contexts, e.g., as a majority or minority culture within a political system. As the group discussions indicate, there are no clear-cut positions anchored in “nationality,” “culture,” or “religion.” Instead, attitudes are personally decided on as part of a negotiated context representing the political, social and existential situatedness of the individual. Therefore, more complex theoretical and practical approaches to cultural diversity have to be developed. (shrink)

BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract (...) screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

It has recently been proposed to incorporate the use of a “Patient Preference Predictor” (PPP) into the process of making treatment decisions for incapacitated patients. A PPP would predict which treatment option a given incapacitated patient would most likely prefer, based on the individual’s characteristics and information on what treatment preferences are correlated with these characteristics. Including a PPP in the shared decision-making process between clinicians and surrogates has the potential to better realize important ethical goals for making treatment decisions (...) for incapacitated patients. However, developing and implementing a PPP poses significant practical challenges. The present paper discusses these practical challenges and considers ways to address them. (shrink)

When a patient lacks sufficient capacity to make a certain treatment decision, whether because of deficits in their ability to make a judgement that reflects their values or to make a decision that reflects their judgement or both, the decision must be made by a surrogate. Often the best way to respect the patient’s autonomy, in such cases, is for the surrogate to make a ‘substituted’ judgement on behalf of the patient, which is the decision that best reflects the patient’s (...) values and identity-infused beliefs as they were when the patient last possessed capacity. It is by now well-known that there is troubling evidence that surrogates do only slightly better than chance at making accurate substituted judgements. The use of a patient preference predictor, an algorithm that predicts patient preferences based on characteristics like the patient’s age, gender, socioeconomic status and education level, could help. Rid and Wendler have done important work making a moral case for using the PPP.1 If it were shown to enhance accuracy, it is not at all obvious what objections there could be to its use. Yet several subtle criticisms of the PPP have emerged in the literature. In their feature article, Jardas, Wasserman and Wendler select six of the most prominent objections to the PPP in the literature and defend the PPP against them.2 Along the way they make significant contributions to broader issues concerning the proper uses of statistical evidence and limits on the kinds of preferences there can be reasons of autonomy to respect. Since I found the authors’ answers to the final two objections—that the PPP relies on naked statistical evidence and that it uses non-endorsed reasons—to be especially interesting and provocative, I will examine those answers and raise a few questions about …. (shrink)

Volume 20, Issue 2, February 2020, Page 66-68.

Large language models (LLMs) such as ChatGPT or Google’s Bard have shown significant performance on a variety of text-based tasks, such as summarization, translation, and even the generation of new...

Progressing beyond the given has been a key modern tendency. Yet modern societies are currently facing the problem of how to put limits on progress, expansion, and growth, live within them, and preserve (rather than transcend) the present. Drawing on economic sociology scholarship on valuation and morality in economic life, this article develops and applies the term economization to analyze the enactment of limits on progress. The question of end-of-life care—when to stop medical efforts to prolong life, postpone death, and (...) advance the scientific frontier—serves as an illustrative empirical case that sheds light on limit-setting in general. My analysis of this case combines historical, ethnographic, and in-depth interview data on US palliative care clinicians, who specialize in making life-and-death decisions in acute care hospitals. (shrink)

One recurring criticism of the best interests standard concerns its vagueness, and thus the inadequate guidance it offers to care providers. The lack of an agreed definition of ‘best interests’, together with the fact that several suggested considerations adopted in legislation or professional guidelines for doctors do not obviously apply across different groups of persons, result in decisions being made in murky waters. In response, bioethicists have attempted to specify the best interests standard, to reduce the indeterminacy surrounding medical decisions. (...) In this paper, we discuss the bioethicists’ response in relation to the state's possible role in clarifying the best interests standard. We identify and characterise two clarificatory strategies employed by bioethicists —elaborative and enumerative—and argue that the state should adopt the latter. Beyond the practical difficulties of the former strategy, a state adoption of it would inevitably be prejudicial in a pluralistic society. Given the gravity of best interests decisions, and the delicate task of respecting citizens with different understandings of best interests, only the enumerative strategy is viable. We argue that this does not commit the state to silence in providing guidance to and supporting healthcare providers, nor does it facilitate the abuse of the vulnerable. Finally, we address two methodological worries about adopting this approach at the state level. The adoption of the enumerative strategy is not defeatist in attitude, nor does it eventually collapse into (a form of) the elaborative strategy. (shrink)

The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients’ preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%–80% reliability of people’s preferences for future medical decisions—a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of (...) persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed. (shrink)

The patient preference predictor is a proposed computer-based algorithm that would predict the treatment preferences of decisionally incapacitated patients. Incorporation of a PPP into the decision-making process has the potential to improve implementation of the substituted judgement standard by providing more accurate predictions of patients’ treatment preferences than reliance on surrogates alone. Yet, critics argue that methods for making treatment decisions for incapacitated patients should be judged on a number of factors beyond simply providing them with the treatments they would (...) have chosen for themselves. These factors include the extent to which the decision-making process recognises patients’ freedom to choose and relies on evidence the patient themselves would take into account when making treatment decisions. These critics conclude that use of a PPP should be rejected on the grounds that it is inconsistent with these factors, especially as they relate to proper respect for patient autonomy. In this paper, we review and evaluate these criticisms. We argue that they do not provide reason to reject use of a PPP, thus supporting efforts to develop a full-scale PPP and to evaluate it in practice. There are no data in this work. (shrink)

The increasing accuracy of algorithms to predict values and preferences raises the possibility that artificial intelligence technology will be able to serve as a surrogate decision-maker for incapacitated patients. Following Camillo Lamanna and Lauren Byrne, we call this technology the autonomy algorithm. Such an algorithm would mine medical research, health records, and social media data to predict patient treatment preferences. The possibility of developing the AA raises the ethical question of whether the AA or a relative ought to serve as (...) surrogate decision-maker in cases where the patient has not issued a medical power of attorney. We argue that in such cases, and against the standard practice of vesting familial surrogates with decision making authority, the AA should have sole decision-making authority. This is because the AA will likely be better at predicting what treatment option the patient would have chosen. It would also be better at avoiding bias and, therefore, choosing in a more patient-centered manner. Furthermore, we argue that these considerations override any moral weight of the patient’s special relationship with their relatives. (shrink)

Volume 20, Issue 2, February 2020, Page 64-66.

In response to a global population with increasingly complex issues at the end of life, a movement in the U.S. has emerged incorporating doulas into end-of-life care. These end-of-life (EOL) doulas are not just focused on the quality of life, but also the quality of death. Like birth doulas, who provide support for pregnant patients and their families, EOL doulas help alleviate physical and mental discomfort in those who are dying. In this paper, we explore the role of EOL doulas (...) in improving the care of unrepresented patients, who lack decision-making capacity and have no surrogates or documents to guide their healthcare decisions. We argue that EOL doulas may help this traditionally underserved population experience a “good death” by answering several ethical and procedural challenges. As quasi-independent, non-medical members of the healthcare team, they provide a balancing, advocating voice on behalf of the patient, and may also help reduce inappropriate treatment, delays in care, and the overburdening of the public guardianship system. As such, attention should be given to formally defining their place within the healthcare infrastructure. Ultimately, we contend that EOL doulas are key to ensuring optimal, ethical care for unrepresented patient populations. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to the (...) lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

When making substituted judgments for incapacitated patients, surrogates often struggle to guess what the patient would want if they had capacity. Surrogates may also agonize over having the (sole) responsibility of making such a determination. To address such concerns, a Patient Preference Predictor (PPP) has been proposed that would use an algorithm to infer the treatment preferences of individual patients from population-level data about the known preferences of people with similar demographic characteristics. However, critics have suggested that even if such (...) a PPP were more accurate, on average, than human surrogates in identifying patient preferences, the proposed algorithm would nevertheless fail to respect the patient’s (former) autonomy since it draws on the ‘wrong’ kind of data: namely, data that are not specific to the individual patient and which therefore may not reflect their actual values, or their reasons for having the preferences they do. Taking such criticisms on board, we here propose a new approach: the Personalized Patient Preference Predictor (P4). The P4 is based on recent advances in machine learning, which allow technologies including large language models to be more cheaply and efficiently ‘fine-tuned’ on person-specific data. The P4, unlike the PPP, would be able to infer an individual patient’s preferences from material (e.g., prior treatment decisions) that is in fact specific to them. Thus, we argue, in addition to being potentially more accurate at the individual level than the previously proposed PPP, the predictions of a P4 would also more directly reflect each patient’s own reasons and values. In this article, we review recent discoveries in artificial intelligence research that suggest a P4 is technically feasible, and argue that, if it is developed and appropriately deployed, it should assuage some of the main autonomy-based concerns of critics of the original PPP. We then consider various objections to our proposal and offer some tentative replies. (shrink)

Purpose Although surrogate decision-making is prevalent in intensive care units and concerns with decision quality are well documented, little is known about how clinicians help family members understand the surrogate role. We investigated whether and how clinicians provide normative guidance to families regarding how to function as a surrogate. Subjects and methods We audiorecorded and transcribed 73 ICU family conferences in which clinicians anticipated discussing goals of care for incapacitated patients at high risk of death. We developed and applied a (...) coding framework to identify normative statements by clinicians regarding what considerations should guide surrogates’ decisions, including whether clinicians explained one or more of Buchanan and Brock’s three standard principles of SDM to family members. Results Clinicians made at least one statement about how to perform the surrogate role in 24 conferences ). We observed three general types of normative guidance provided to surrogates, with some conferences containing more than one type of guidance: counselling about one or more standard principles of SDM ; counselling surrogates to make decisions centred on the patient as a person, without specifying how to accomplish that ; and counselling surrogates to make decisions based on the family’s values. Conclusions Clinicians did not provide normative guidance about the surrogate role in two-thirds of family conferences for incapacitated patients at high risk for death. When they did, clinicians’ guidance was often incomplete and sometimes conflicted with standard principles of SDM. Future work is needed to understand whether providing explicit guidance on how to perform the surrogate role improves decision-making or mitigates surrogates’ psychological distress. (shrink)

Purpose Although surrogate decision-making is prevalent in intensive care units and concerns with decision quality are well documented, little is known about how clinicians help family members understand the surrogate role. We investigated whether and how clinicians provide normative guidance to families regarding how to function as a surrogate. Subjects and methods We audiorecorded and transcribed 73 ICU family conferences in which clinicians anticipated discussing goals of care for incapacitated patients at high risk of death. We developed and applied a (...) coding framework to identify normative statements by clinicians regarding what considerations should guide surrogates’ decisions, including whether clinicians explained one or more of Buchanan and Brock’s three standard principles of SDM to family members. Results Clinicians made at least one statement about how to perform the surrogate role in 24 conferences ). We observed three general types of normative guidance provided to surrogates, with some conferences containing more than one type of guidance: counselling about one or more standard principles of SDM ; counselling surrogates to make decisions centred on the patient as a person, without specifying how to accomplish that ; and counselling surrogates to make decisions based on the family’s values. Conclusions Clinicians did not provide normative guidance about the surrogate role in two-thirds of family conferences for incapacitated patients at high risk for death. When they did, clinicians’ guidance was often incomplete and sometimes conflicted with standard principles of SDM. Future work is needed to understand whether providing explicit guidance on how to perform the surrogate role improves decision-making or mitigates surrogates’ psychological distress. (shrink)