Ezio Di Nucci

This article provides an in-depth analysis of the wrongness of killing by comparing different versions of three influential views: the traditional view that killing is always wrong; the liberal view that killing is wrong if and only if the victim does not want to be killed; and Don Marquis’ future of value account of the wrongness of killing. In particular, I illustrate the advantages that a basic version of the liberal view and a basic version of the future of value account have over competing alternatives. Still, ultimately none of the views analysed here are satisfactory; but the different reasons why those competing views fail provide important insights into the ethics of killing.

Should involuntarily childless people have the same opportunities to access parenthood as those who are not involuntarily childless? In the context of assisted reproductive technologies, affirmative answers to this question are often cashed out in terms of positive rights, including rights to third-party reproduction. In this paper, we critically explore the scope and extent to which any such right would hold up morally. Ultimately, we argue for a departure away from positive parental rights. Instead, we argue that the state has an imperfect duty to benefit involuntarily childless people in relation to their parental aspirations.

Should involuntarily childless people have the sameopportunities to access parenthood as those who are not involuntarily childless? In the context of assisted reproductive technologies, affirmative answers to this question are often cashed out in terms of positive rights, including rights to third-party reproduction. In this paper, wecritically explore the scope and extent to which any such right would hold up morally. Ultimately, we argue for a departure away from positive parental rights. Instead, we argue that the state has an imperfect duty to benefit involuntarily childless people in relation to their parental aspirations.

We analyze Denmark’s COVID-19 containment policies. We argue that, despite the precautionary principle being explicitly appealed to by decision-makers at the highest political level, it is neither clear whether Danish COVID-19 policies did in fact constitute a genuine application of the precautionary principle, nor is it clear that the particular restrictions implemented ought indeed to count as precautionary when seen from a perspective that transcends the short-term emergency. Finally, we point at evidence suggesting that lock down policies had the effect of saving the primarily older group of potential COVID-19 victims while causing severe problems of loneliness, mental illness, loss of education and loss of life opportunities for children and young people. This raises serious questions of social and intergenerational injustice in relation to lock down policies

Frankfurt sets out to refute the principle according to which ‘a person is morally responsible for what he has done only if he could have done otherwise’. Frankfurt devises a counterexample in which an agent is intuitively responsible even though she could not have done otherwise: Suppose someone – Black, let us say – wants Jones to perform a certain action. Black is prepared to go to considerable lengths to get his way, but he prefers to avoid showing his hand unnecessarily. So he waits until Jones is about to make up his mind what to do, and he does nothing unless it is clear to him that Jones is going to decide to do something other than what he wants him to do. If it does become clear that Jones is going to decide to do something else, Black takes effective steps to ensure that Jones decides to do, and that he does do, what he wants him to do. Whatever Jones's initial preferences and inclinations, then, Black will have his way … Now suppose that Black never has to show his hand because Jones, for reasons of his own, decides to perform and does perform the very action Black wants him to perform. In that case, it seems clear, Jones will bear precisely …

I analyse the tension between a plausible liberal view of sex work and the similarly plausible idea that rape and other forms of sexual violence are made morally worse by their sexual nature. I find no conclusive reason to drop the liberal view of sex work, at least as long as the concept of voluntary and informed consent at the core of it is robust enough to account for the realities of prostitution around the world; nor should we abandon the idea that rape is no ordinary immoral act: reducing sexual violence to non-sexual violence would misrepresent the relevant phenomena and perpetuate injustice.

According to the Simple View of intentional action, I have intentionally switched on the light only if I intended to switch on the light. The idea that intending to is necessary for intentionally -ing has been challenged by Bratman (1984, 1987) with a counter-example in which a videogame player is trying to hit either of two targets while knowing that she cannot hit both targets. When a target is hit, the game finishes. And if both targets are about to be hit simultaneously, the game shuts down. The player knows that she cannot hit both targets, but still she concludes that, given her skills, the best strategy is to have a go at each target at the same time. Suppose she hits target 1. It seems obvious that she has hit target 1 intentionally. But, Bratman argues, she could not have intended to hit target 1. Since the scenario is perfectly symmetrical, had the player intended to hit target 1, she would have also had to intend to hit target 2. But the player knows that she cannot hit both targets.

Machine learning platforms have emerged as a new promissory technology that some argue will revolutionize work practices across a broad range of professions, including medical care. During the past few years, IBM has been testing its Watson for Oncology platform at several oncology departments around the world. Published reports, news stories, as well as our own empirical research show that in some cases, the levels of concordance over recommended treatment protocols between the platform and human oncologists have been quite low. Other studies supported by IBM claim concordance rates as high as 96%. We use the Watson for Oncology case to examine the practice of using concordance levels between tumor boards and a machine learning decision-support system as a form of evidence. We address a challenge related to the epistemic authority between oncologists on tumor boards and the Watson Oncology platform by arguing that the use of concordance levels as a form of evidence of quality or trustworthiness is problematic. Although the platform provides links to the literature from which it draws its conclusion, it obfuscates the scoring criteria that it uses to value some studies over others. In other words, the platform “black boxes” the values that are coded into its scoring system.

This bioethics handbook offers concise, up-to-date, and easy to read chapters on a broad range of bioethical topics in the following categories: foundational concepts, theory and method, healthcare ethics, research ethics, public health, technology, and the environment. The volume provides a snapshot of current bioethics, taking into account current affairs and emerging new topics. Each chapter acknowledges and critically breaks down the historical developments of the subject and the most authoritative existing literature on respective topics, providing accessible and up-to-date philosophical analysis. As such, the chapters are designed to be attractive as primary or supplementary teaching material for university classes of the philosophical or bioethical variety, with clear demarcations and indicators for key terms, ideas, and arguments that should also facilitate productive note-taking and points for critical discussion for students. The handbook also serves as a one-stop starting resource for multi- and interdisciplinary researchers and practitioners who engage with bioethics in their work.

This paper argues that we ought to distinguish between ‘assisted’ gestation and ‘delegating’ gestation—and that the relevant difference does not depend on whether it is another human or technological system doing the work.1 In the philosophy of action, there is an important theoretical gap between S ‘helping A to φ’ and S ‘φ-ing on behalf of A’: the former is an instance of joint agency while the latter is an individual’s action. This matters because if the latter counts as an intentional action, then only S is being said to have acted intentionally while, if the former is an intentional action, then both S and A have acted intentionally—which might have consequences for responsibility attributions, for example. To put it brutally, it is the difference between Belarus supporting Putin’s invasion and Belarus doing Putin’s dirty work—and you do not have to be a Russian mother to see that these two alternatives will have very different consequences. In another context, the economics of outsourcing is also built around the same theoretical distinction. But does this matter to gestation and reproduction? It does, argues this paper. Insisting on an ‘assisted’ reproduction framework2–8 reinforces patriarchal stereotypes and continues to put undue burdens on prospective mothers both by perpetuating the stereotype of women as needing help and also by implicating them in an action that might genuinely not be theirs. Delegation, on the other hand, empowers parents—who are in charge, rather than needing help. Furthermore, an action-theoretical delegation framework might be better placed to regulate and anticipate future technological developments, …

I analyse an argument according to which medical artificial intelligence represents a threat to patient autonomy—recently put forward by Rosalind McDougall in the Journal of Medical Ethics. The argument takes the case of IBM Watson for Oncology to argue that such technologies risk disregarding the individual values and wishes of patients. I find three problems with this argument: it confuses AI with machine learning; it misses machine learning’s potential for personalised medicine through big data; it fails to distinguish between evidence-based advice and decision-making within healthcare. I conclude that how much and which tasks we should delegate to machine learning and other technologies within healthcare and beyond is indeed a crucial question of our time, but in order to answer it, we must be careful in analysing and properly distinguish between the different systems and different delegated tasks.