This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements (...) in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)

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...

This paper explores an emerging sub-field of both empirical bioethics and experimental philosophy, which has been called “experimental philosophical bioethics” (bioxphi). As an empirical discipline, bioxphi adopts the methods of experimental moral psychology and cognitive science; it does so to make sense of the eliciting factors and underlying cognitive processes that shape people’s moral judgments, particularly about real-world matters of bioethical concern. Yet, as a normative discipline situated within the broader field of bioethics, it also aims to contribute to substantive (...) ethical questions about what should be done in a given context. What are some of the ways in which this aim has been pursued? In this paper, we employ a case study approach to examine and critically evaluate four strategies from the recent literature by which scholars in bioxphi have leveraged empirical data in the service of normative arguments. (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)

We describe a form of moral artificial intelligence that could be used to improve human moral decision-making. We call it the “artificial moral advisor”. The AMA would implement a quasi-relativistic version of the “ideal observer” famously described by Roderick Firth. We describe similarities and differences between the AMA and Firth’s ideal observer. Like Firth’s ideal observer, the AMA is disinterested, dispassionate, and consistent in its judgments. Unlike Firth’s observer, the AMA is non-absolutist, because it would take into account the human (...) agent’s own principles and values. We argue that the AMA would respect and indeed enhance individuals’ moral autonomy, help individuals achieve wide and a narrow reflective equilibrium, make up for the limitations of human moral psychology in a way that takes conservatives’ objections to human bioenhancement seriously, and implement the positive functions of intuitions and emotions in human morality without their downsides, such as biases and prejudices. (shrink)

Obtaining informed consent from patients prior to a medical or surgical procedure is a fundamental part of safe and ethical clinical practice. Currently, it is routine for a significant part of the consent process to be delegated to members of the clinical team not performing the procedure (eg, junior doctors). However, it is common for consent-taking delegates to lack sufficient time and clinical knowledge to adequately promote patient autonomy and informed decision-making. Such problems might be addressed in a number of (...) ways. One possible solution to this clinical dilemma is through the use of conversational artificial intelligence using large language models (LLMs). There is considerable interest in the potential benefits of such models in medicine. For delegated procedural consent, LLM could improve patients’ access to the relevant procedural information and therefore enhance informed decision-making.In this paper, we first outline a hypothetical example of delegation of consent to LLMs prior to surgery. We then discuss existing clinical guidelines for consent delegation and some of the ways in which current practice may fail to meet the ethical purposes of informed consent. We outline and discuss the ethical implications of delegating consent to LLMs in medicine concluding that at least in certain clinical situations, the benefits of LLMs potentially far outweigh those of current practices. (shrink)

Simulations are used in very different contexts and for very different purposes. An emerging development is the possibility of using simulations to obtain a more or less representative reproduction of organs or even entire persons. Such simulations are framed and discussed using the term ‘digital twin’. This paper unpacks and scrutinises the current use of such digital twins in medicine and the ideas embedded in this practice. First, the paper maps the different types of digital twins. A special focus is (...) put on the concrete challenges inherent in the interactions between persons and their digital twin. Second, the paper addresses the questions of how far a digital twin can represent a person and what the consequences of this may be. Against the background of these two analytical steps, the paper defines first conditions for digital twins to take on an ethically justifiable form of representation. (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 standard approach to treatment decision making for incapacitated patients often fails to provide treatment consistent with the patient’s preferences and values and places significant stress on surrogate decision makers. These shortcomings provide compelling reason to search for methods to improve current practice. Shared decision making between surrogates and clinicians has important advantages, but it does not provide a way to determine patients’ treatment preferences. Hence, shared decision making leaves families with the stressful challenge of identifying the patient’s preferred treatment (...) option. To address this concern, the present paper proposes to incorporate the use of a “Patient Preference Predictor” (PPP) into the shared decision-making process between surrogates and clinicians. 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. Use of a PPP is likely to increase the chances that incapacitated patients are treated consistent with their preferences and values and might reduce the stress and burden on their surrogates. Including a PPP in the shared decision-making process therefore has the potential to realize important ethical goals for making treatment decisions for incapacitated patients. The present paper justifies this approach on conceptual and normative grounds. (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around (...) cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

Background Advance healthcare decision-making presumes that a prior treatment preference expressed with sufficient mental capacity (“T1 preference”) should trump a contrary preference expressed after significant cognitive decline (“T2 preference”). This assumption is much debated in normative bioethics, but little is known about lay judgments in this domain. This study investigated participants’ judgments about which preference should be followed, and whether these judgments differed depending on a first-person (deciding for one’s future self) versus third-person (deciding for a friend or stranger) perspective. (...) -/- Methods A vignette-based survey was conducted (N = 1445 US Americans; gender-balanced sample), in a 3 (relationship: self, best friend, stranger) × 2 (T1 preference: treat, do not treat) × 2 (T2 contrary preference: ambiguous, unambiguous) design. -/- Results Participants were more likely to defer to the incapacitated T2 preference of a third-party, while being more likely to insist on following their own T1 capacitated preference. Further, participants were more likely to conclude that others with substantial cognitive decline were still their “true selves,” which correlated with increased deference to their T2 preferences. -/- Conclusions These findings add to the growing evidence that lay intuitions concerning the ethical entitlement to have decisions respected are not only a function of cognition, as would be expected under many traditional bioethical accounts, but also depend on the relationship of the decision to the decision-maker’s true self. (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)

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)

In our recent article ‘The Ethics of the Algorithmic Prediction of Goal of Care Preferences: From Theory to Practice’1, we aimed to ignite a critical discussion on why and how to design artificial intelligence (AI) systems assisting clinicians and next-of-kin by predicting goal of care preferences for incapacitated patients. Here, we would like to thank the commentators for their valuable responses to our work. We identified three core themes in their commentaries: (1) the risks of AI paternalism, (2) worries about (...) attacks to our humanity stemming from the use of AI and (3) the possibility of designing AI systems for more relevant use cases than the one we consider in our work. We shall focus on these themes, leaving aside some other interesting suggestions, given the limited space available for our response. Diaz Milian and Bhattacharyya discuss the risks of AI paternalism highlighting how the use of an AI to predict goal of care preferences may incentivise clinicians and surrogates to shift the burden of decision making to the machine.2 In particular, ‘[i]f the AI-generated response is given priority above the other agents’2 and the AI makes decisions autonomously, then ‘AI paternalism’ would emerge.2 Against this possibility, the authors suggest four safeguards to be implemented in the AI life-cycle.2 These are procedures that may improve the trustworthiness of the AI and human control over it.3 We agree that it is important …. (shrink)

How shall we decide for others who cannot decide for themselves? And who—or what, in the case of artificial intelligence — should make the decision? The present issue of the journal tackles several interrelated topics, many of them having to do with surrogate decision making. For example, the feature article by Jardas et al 1 explores the potential use of artificial intelligence to predict incapacitated patients’ likely treatment preferences based on their sociodemographic characteristics, raising questions about the means by which (...) we come to decide for others. And a clinical ethics round table led by Wilkinson and Pillay2 examines the case of a premature baby on life support whose primary surrogate is herself incapacitated. Together, these examples force us to think more deeply about the meaning and significance of taken-for-granted concepts: respect for autonomy, substituted judgement, best interests. We’ll consider the baby first and then turn to AI. “Baby T” is a critically ill newborn delivered prematurely by emergency caesarean section. The mother had entered into a surrogacy arrangement with a same-sex male couple, the intended parents, who were to take over the baby’s care after birth—just as soon as a formal parental order could be obtained through the court. Until then, the birth mother, who had used her own eggs to conceive Baby T along with sperm from an unidentified donor, would have legal and ethical responsibility to decide about the baby’s care. Unfortunately, she too was in critical condition, having fallen unconscious prior to delivery due to a sudden brain haemorrhage. She remained unconscious, and thus incapacitated, during a crucial period in which time-sensitive decisions about Baby T’s care needed to be made, including whether to continue life support. Given the mother’s incapacity, who should …. (shrink)

The question of what makes someone the same person through time and change has long been a preoccupation of philosophers. In recent years, the question of what makes ordinary or lay people judge that someone is—or isn’t—the same person has caught the interest of experimental psychologists. These latter, empirically oriented researchers have sought to understand the cognitive processes and eliciting factors that shape ordinary people’s judgments about personal identity and the self. Still more recently, practitioners within an emerging discipline, experimental (...) philosophical bioethics or “bioxphi”—the focus of this chapter—have adopted a similar aim and employed similar methodologies, but with two distinctive features: (a) a special concern for enhanced ecological validity in the examples and populations studied; and (b) an interest in contributing to substantive normative debates within the wider field of bioethics. Our aim in this chapter is to sample illustrative work on personal identity in bioxphi, explore how it relates to studies in psychology covering similar terrain, and draw out the implications of this work for matters of bioethical concern. In pursuing these issues, we highlight recent work in bioxphi that includes the perceived validity of advance directives following neurodegeneration, the right of psychologically altered study participants to withdraw from research, how drug addiction may cause one to be regarded by others as “a completely different person,” the effect of deep-brain stimulation on perceptions of the self, and the potential influence of moral enhancement interventions on intuitive impressions of a person’s character. (shrink)

Like many, I find the idea of relying on patient preference predictors in life-or-death cases ethically troubling. As part of his stimulating discussion, Sharadin1 diagnoses such unease as a worry that using PPPs disrespects patients’ autonomy, by treating their most intimate and significant desires as if they were caused by their demographic traits. I agree entirely with Sharadin’s ‘debunking’ response to this concern: we can use statistical correlations to predict others’ preferences without thereby assuming any causal claim. However, I suspect (...) that, for at least some of us, our unease about PPPs stems from a different kind of ‘autonomy’ concern. In this commentary, then, I will explore this concern, and show how it relates to Sharadin’s work. Very many of our preferences are caused, ultimately, by facts which are outside our control, such as our demographic features. However, I suggest that we can still act autonomously on the basis of such preferences, when they are preferences which we endorse. Imagine, for example, that Jane has grown up in a church-growing environment, which has shaped many of her preferences, …. (shrink)

In this paper, I set out two ethical complications for Rid and Wendler’s proposal that a “Patient Preference Predictor” (PPP) should be used to aid decision making about incapacitated patients’ care. Both of these worries concern how a PPP might categorize patients. In the first section of the paper, I set out some general considerations about the “ethics of apt categorization” within stratified medicine and show how these challenge certain PPPs. In the second section, I argue for a more specific—but (...) more contentious—claim that proper respect for the autonomy of incapacitated patients might require us to act on reasons which they could endorse and show how this claim places important limits on the categories employed by an ethically acceptable PPP. The conclusion shows how these concerns about apt categorization relate to more familiar worries about Rid and Wendler’s proposals. (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)

Dementia is highly prevalent and incurable. The participation of dementia patients in clinical research is indispensable if we want to find an effective treatment for dementia. However, one of the primary challenges in dementia research is the patients’ gradual loss of the capacity to consent. Patients with dementia are characterized by the fact that, at an earlier stage of their life, they were able to give their consent to participation in research. Therefore, the phase when patients are still competent to (...) decide offers a valuable opportunity to authorize research, by using an advance research directive. Yet, the use of ARDs as an authorization for research participation remains controversial. In this paper we discuss the role of autonomous decision-making and the protection of incompetent research subjects. We will show why ARDs are a morally defensible basis for the inclusion of this population in biomedical research and that the use of ARDs is compatible with the protection of incompetent research subjects. (shrink)

Background Artificial intelligence (AI) has revolutionized various healthcare domains, where AI algorithms sometimes even outperform human specialists. However, the field of clinical ethics has remained largely untouched by AI advances. This study explores the attitudes of anesthesiologists and internists towards the use of AI-driven preference prediction tools to support ethical decision-making for incapacitated patients. Methods A questionnaire was developed and pretested among medical students. The questionnaire was distributed to 200 German anesthesiologists and 200 German internists, thereby focusing on physicians who (...) often encounter patients lacking decision-making capacity. The questionnaire covered attitudes toward AI-driven preference prediction, availability and utilization of Clinical Ethics Support Services (CESS), and experiences with ethically challenging situations. Descriptive statistics and bivariate analysis was performed. Qualitative responses were analyzed using content analysis in a mixed inductive-deductive approach. Results Participants were predominantly male (69.3%), with ages ranging from 27 to 77. Most worked in nonacademic hospitals (82%). Physicians generally showed hesitance toward AI-driven preference prediction, citing concerns about the loss of individuality and humanity, lack of explicability in AI results, and doubts about AI’s ability to encompass the ethical deliberation process. In contrast, physicians had a more positive opinion of CESS. Availability of CESS varied, with 81.8% of participants reporting access. Among those without access, 91.8% expressed a desire for CESS. Physicians' reluctance toward AI-driven preference prediction aligns with concerns about transparency, individuality, and human-machine interaction. While AI could enhance the accuracy of predictions and reduce surrogate burden, concerns about potential biases, de-humanisation, and lack of explicability persist. Conclusions German physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS. Addressing concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics. Further research into patient and surrogate perspectives is needed to ensure AI aligns with patient preferences and values in complex medical decisions. (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)

Recently, Jardas _et al_ have convincingly defended the patient preference predictor (PPP) against a range of autonomy-based objections. In this response, I propose a new autonomy-based objection to the PPP that is not explicitly discussed by Jardas _et al_. I call it the ‘objection from higher-order preferences’. Even if this objection is not sufficient reason to reject the PPP, the objection constitutes a pro tanto reason that is at least as powerful as the ones discussed by Jardas _et al._.

There are substantial data establishing that surrogates are often mistaken in predicting what treatments incompetent patients would have wanted and that supplements such as advance directives have not resulted in significant improvements. Rid and Wendler’s Patient Preference Predictor (PPP) proposal will attempt to gather data about what similar patients would prefer in a variety of treatment choices. It accepts the usual goal of patient autonomy and the Substituted Judgment principle for surrogate decisions. I provide reasons for questioning sole reliance on (...) autonomy when surrogates, not patients, are deciding and for giving some independent weight to patient well-being. Moreover, that family members, typical surrogates, will know best what the patient if incompetent would have wanted is not the only reason why they are chosen. The more pressing problem is that the PPP would fail to remove the more serious mistakes that empirical psychology over the last few decades has shown to infect such decision making. (shrink)

The Patient Preference Predictor (PPP) is intended to improve treatment decision making for incapacitated patients. The PPP would collect information about the treatment preferences of people with different demographic and other characteristics. It could be used to indicate which treatment option an individual patient would be most likely to prefer, based on data about the preferences of people who resemble the patient. The PPP could be incorporated into existing US law governing treatment for incapacitated patients, although it is unclear whether (...) it would be classified as evidence of a specific patient’s preferences or those of a reasonable person sharing certain characteristics with the patient. Ethical concerns about the quality and significance of PPP choices could influence legal decision makers’ views of the PPP. (shrink)

The authority of surrogates—often close family members—to make treatment decisions for previously capacitated patients is said to come from their knowledge of the patient, which they are to draw on as they exercise substituted judgment on the patient’s behalf. However, proxy accuracy studies call this authority into question, hence the Patient Preference Predictor (PPP). We identify two problems with contemporary understandings of the surrogate’s role. The first is with the assumption that knowledge of the patient entails knowledge of what the (...) patient’s choice of treatment would be. The second is with the assumption that a good decision reproduces the content of that choice. If we are right, then the PPP, helpful though it might be in guiding surrogates’ decisions, nevertheless would hold them to the wrong standards and in that way could add to, rather than relieve, the stress they experience as they try to do their job. (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)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the kind of broad (...) spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)

The authors respond to four JME commentaries on their Feature Article, ‘Autonomy-based criticisms of the patient preference predictor’.

In the article ‘Represent Me: Please! Towards an Ethics of Digital Twins in Medicine’, I analysed and tried to better understand the main ethical challenges associated with Digital Twins. For those who are just entering the debate with this article: DT is a metaphor for a bundle of artificial intelligence driven simulation technologies that constantly, in real time and ad personam simulate single or multiple parts of the body and make predictions about future health states based on these simulations. My (...) argument addresses the need to look at the breadth of challenges through a perspective of representation. From such a take, key consequences can be drawn for the further development of such technologies, the required means of control and not least the way to think about the interaction between humans and ‘their’ simulations. I am very grateful for the subsequent suggestions, criticisms and further arguments, which have helped me to elaborate my arguments more clearly and have led to a substantial advancement of the debate and the discussion about ethical questions in dealing with DT. Jenny Blumenthal-Barby,1 Deborah Lupton,2 Jenny Krutzinna,3 Janina Loh,4 Brent Mittelstadt,5 Sven Nyholm,6 Daniel Tigard7 and Max Tretter8 have made excellent points, which, in the following, I will not be able to reply to individually and in detail. In this response, I will try to bundle the open points and questions along the comments and considerations mentioned above and attempt to outline their significance for an ethics of DT. Four challenges seem to be central. A first point addresses anthropological issues. It is concerned …. (shrink)