S. Andrew Schroeder

There is virtually no philosophical consensus on what, exactly, imperfect duties are. In this paper, I lay out three criteria which I argue any adequate account of imperfect duties should satisfy. Using beneficence as a leading example, I suggest that existing accounts of imperfect duties will have trouble meeting those criteria. I then propose a new approach: thinking of imperfect duties as duties held by groups, rather than individuals. I show, again using the example of beneficence, that this proposal can satisfy the criteria, explaining how something can both have the necessity characteristic of duty, while also allowing agents the latitude which seems to attach to imperfect duties.

Recently, a number of philosophers have argued that we can and should “consequentialize” non-consequentialist moral theories, putting them into a consequentialist framework. I argue that these philosophers, usually treated as a group, in fact offer three separate arguments, two of which are incompatible. I show that none represent significant threats to a committed non-consequentialist, and that the literature has suffered due to a failure to distinguish these arguments. I conclude by showing that the failure of the consequentializers’ arguments has implications for disciplines, such as economics, logic, decision theory, and linguistics, which sometimes use a consequentialist structure to represent non-consequentialist ethical theories.

There is a growing consensus among philosophers of science that core parts of the scientific process involve non-epistemic values. This undermines the traditional foundation for public trust in science. In this article I consider two proposals for justifying public trust in value-laden science. According to the first, scientists can promote trust by being transparent about their value choices. On the second, trust requires that the values of a scientist align with the values of an individual member of the public. I argue that neither of these proposals work and suggest an alternative that does better. When scientists must appeal to values in the course of their research, they should appeal to democratic values: the values of the public or its representatives.

Much academic work (in philosophy, economics, law, etc.), as well as common sense, assumes that ill health reduces well-being. It is bad for a person to become sick, injured, disabled, etc. Empirical research, however, shows that people living with health problems report surprisingly high levels of well-being - in some cases as high as the self-reported well-being of healthy people. In this chapter, I explore the relationship between health and well-being. I argue that although we have good reason to believe that health problems causing pain and death typically do reduce well-being, health problems that limit capabilities probably don't reduce well-being nearly as much as most people suppose. I then briefly explore the consequences of this conclusion for political philosophy and ethics. If many health problems don't significantly reduce well-being, why should governments go to great expense to prevent or treat them? Why should parents be obliged to ensure the health of their children?

Theorists of health have, to this point, focused exclusively on trying to define a state—health—that an organism might be in. I argue that they have overlooked the possibility of a comparativist theory of health, which would begin by defining a relation—healthier than—that holds between two organisms or two possible states of the same organism. I show that a comparativist approach to health has a number of attractive features, and has important implications for philosophers of medicine, bioethicists, health economists, and policy makers

Define teleology as the view that requirements hold in virtue of facts about value or goodness. Teleological views are quite popular, and in fact some philosophers (e.g. Dreier, Smith) argue that all (plausible) moral theories can be understood teleologically. I argue, however, that certain well-known cases show that the teleologist must at minimum assume that there are certain facts that an agent ought to know, and that this means that requirements can't, in general, hold in virtue of facts about value or goodness. I then show that even if we grant those 'ought's teleology still runs into problems. A positive justification of teleology looks like it will require an argument of this form: O(X); if X, then O(Y); therefore O(Y). But this form of argument isn't in general valid. I conclude by offering two positive suggestions for those attracted to a teleological outlook.

Philosophers of science now broadly agree that doing good science involves making non-epistemic value judgments. I call attention to two very different normative standards which can be used to evaluate such judgments: standards grounded in ethics and standards grounded in political philosophy. Though this distinction has not previously been highlighted, I show that the values in science literature contain arguments of each type. I conclude by explaining why this distinction is important. Seeking to determine whether some value-laden determination meets substantive ethical standards is a very different endeavor from seeking to determine if it is politically legitimate.

What makes science trustworthy to the public? This chapter examines one proposed answer: the trustworthiness of science is based at least in part on its independence from the idiosyncratic values, interests, and ideas of individual scientists. That is, science is trustworthy to the extent that following the scientific process would result in the same conclusions, regardless of the particular scientists involved. We analyze this "idiosyncrasy-free ideal" for science by looking at philosophical debates about inductive risk, focusing on two recent proposals which offer different methods of avoiding idiosyncrasy: the high epistemic standards proposal and the democratic values proposal.

Scientists have the ability to influence policy in important ways through how they present their results. Surprisingly, existing codes of scientific ethics have little to say about such choices. I propose that we can arrive at a set of ethical guidelines to govern scientists’ presentation of information to policymakers by looking to bioethics: roughly, just as a clinician should aim to promote informed decision-making by patients, a scientist should aim to promote informed decision-making by policymakers. Though this may sound like a natural proposal, I show it offers guidance that conflicts with standard scientific practices. I conclude by considering one cost of the proposal: that it would prevent scientists from acting as advocates in a way that is currently common in certain fields. I accept that the proposal would restrict scientists’ political advocacy rights, but argue that the benefits of adopting it — promoting democratic governance — justify the restriction.

Many economic measures are structured to reflect ethical values. I describe three attitudes towards this: maximalism, according to which we should aim to build all relevant values into measures; minimalism, according to which we should aim to keep values out of measures; and an intermediate view. I argue the intermediate view is likely correct, but existing versions are inadequate. In particular, economists have strong reason to structure measures to reflect fixed, as opposed to user-assessable, values. This implies that, despite disagreement about precisely how to do so, economists should standardly adjust QALYs and DALYs to reflect egalitarian values.

I compare two different arguments for the importance of bringing new voices into science: arguments for increasing the representation of women, and arguments for the inclusion of the public, or for “citizen science”. I suggest that in each case, diversifying science can improve the quality of scientific results in three distinct ways: epistemically, ethically, and politically. In the first two respects, the mechanisms are essentially the same. In the third respect, the mechanisms are importantly different. Though this might appear to suggest a broad similarity between the cases, I show that the analysis reveals an important respect in which efforts to include the public are more complex. With citizen science programs, unlike with efforts to bring more women into science, the three types of improvement are often in conflict with one another: improvements along one dimension may come at a cost on another dimension, suggesting difficult trade-offs may need to be made.

In this paper I look at the much-discussed case of disabled parents seeking to conceive disabled children. I argue that the permissibility of selecting for disability does not depend on the precise impact the disability will have on the child’s wellbeing. I then turn to an alternative analysis, which argues that the permissibility of selecting for disability depends on the impact that disability will have on the child’s future opportunities. Nearly all bioethicists who have approached the issue in this way have argued that disabilities like deafness unacceptably constrain a child’s opportunities. I argue, however, that this conclusion is premature for several reasons. Most importantly, we don’t have a good way of comparing opportunity sets. Thus, we can’t conclude that deaf children will grow up to have a constrained set of opportunities relative to hearing children. I conclude by suggesting that bioethicists and philosophers of disability need to spend more time thinking carefully about the relationship between disability and opportunity.

Scientists are frequently called upon to “democratize” science, by bringing the public into scientific research. One appealing point for public involvement concerns the nonepistemic values involved in science. Suppose, though, a scientist invites the public to participate in making such value-laden determinations but finds that the public holds values the scientist considers morally unacceptable. Does the argument for democratizing science commit the scientist to accepting the public’s objectionable values, or may she veto them? I argue that there are a limited set of cases in which scientists can, consistently with a commitment to democratized science, set aside the public’s judgments.

When disability-adjusted life years are used to measure the burden of disease on a population in a time interval, they can be calculated in several different ways: from an incidence, pure prevalence, or hybrid perspective. I show that these calculation methods are not equivalent and discuss some of the formal difficulties each method faces. I show that if we don’t discount the value of future health, there is a sense in which the choice of calculation method is a mere question of accounting. Such questions can be important, but they don’t raise deep theoretical concerns. If we do discount, however, choice of calculation method can change the relative burden attributed to different conditions over time. I conclude by recommending that studies involving disability-adjusted life years be explicit in noting what calculation method is being employed and in explaining why that calculation method has been chosen.

Summary measures of health, such as the quality-adjusted life year and disability-adjusted life year, have long been known to incorporate a number of value choices. In this paper, though, I show that the value choices in the construction of such measures extend far beyond what is generally recognized. In showing this, I hope both to improve the understanding of those measures by epidemiologists, health economists and policy-makers, and also to contribute to the general debate about the extent to which such measures should be adjusted to reflect ethical values.

Many philosophers of science have argued that social and ethical values have a significant role to play in core parts of the scientific process. This naturally suggests the following question: when such value choices need to be made, which or whose values should be used? A common answer to this question turns to democratic values—the values of the public or its representatives. I argue that this imposes a morally significant burden on certain scientists, effectively requiring them to advocate for policy positions they strongly disagree with. I conclude by discussing under what conditions this burden might be justified.

The Global Burden of Disease Study is one of the largest-scale research collaborations in global health, producing critical data for researchers, policy-makers, and health workers about more than 350 diseases, injuries, and risk factors. Such an undertaking is, of course, extremely complex from an empirical perspective. But it also raises complex ethical and philosophical questions. In this volume, a group of leading philosophers, economists, epidemiologists, and policy scholars identify and discuss these philosophical questions. Better appreciating the philosophical dimensions of a study like the GBD can make possible a more sophisticated interpretation of its results, and it can improve epidemiological studies in the future, so that they are better suited to produce results that help us to improve global health.

Mainstream views of medical research tell us it should be a fact-based, value-free endeavor: what a scientist (or her funding source) wants or cares about should not influence her findings. At the same time, we also sometimes criticize medical research for failing to embody certain values, e.g. when we criticize pharmaceutical companies for largely ignoring the diseases that affect the global poor. This chapter seeks to reconcile these perspectives by distinguishing appropriate from inappropriate influences of values on medical research. It divides this broad question into two narrower ones, the Role Question (at what points in the research process is value influence potentially acceptable?) and the Content Question (when value influence is potentially acceptable, what specific values should researchers use?), and then draws on the philosophical literature on values in science to explore answers to each of them.

I look at the two main approaches used to count COVID-19 deaths and show how each of those approaches can appear to both overcount COVID deaths (including deaths it should exclude) and undercount COVID deaths (excluding deaths it should include). I trace this to the fact - well-known to philosophers - that causal attribution is interest-relative. Which deaths we should attribute to COVID (as opposed to other causes) will depend on our particular interests and values. Contrary to what many journalists and researchers report, there is therefore no such thing as the "true" COVID death toll. Understanding this can help us to become more sophisticated consumers of COVID information. I conclude by suggesting that scientists, by reflecting on society's varied interests and values, are in a position to construct measures of COVID-caused mortality that are potentially as useful, or more useful, than the measures we currently have.

In their insightful article, ‘The Disvalue of Death in the Global Burden of Disease’, Solberg et al argue that there is a potential incoherence in the way disability-adjusted life years are calculated. Morbidity is measured in years lived with disability in a way quite unlike the way mortality is measured in years of life lost. This potentially renders them incommensurable, like apples and oranges, and makes their aggregate—DALYs—conceptually unsound. The authors say that it is ‘vital’ to address this problem, that ‘[n]eglecting [it] is not an option’, and that ‘one cannot add YLLs and YLDs together in [their] current form’.1 Though one might object to their argument in various ways, let us assume the authors are correct that there is a potential inconsistency here. I want to ask why we should be troubled by that. Now, this question may scarcely seem worth asking. Consistency and coherence are regarded as non-negotiable in so many domains that of course we should demand them in a measure such as the DALY. And this desire for coherence is clearly shared by both the architects of the DALY and their critics.2 3 I think, though, that things are more complicated. In 2012, I wrote an article discussing the epidemiological perspective from which DALYs should be calculated.4 Simplifying greatly, here is the problem: both YLDs and YLLs can be calculated either from prevalence data or from incidence data. The most natural way to calculate YLDs bases them on prevalences, because when we think about morbidity our initial thought is of people who are suffering. The most natural way to calculate YLLs bases them on an incidence measure, because when …

This chapter suggests that descriptive epidemiological studies like the Global Burden of Disease Study can usefully be divided into four tasks: describing individuals’ health states over time, assessing their health states under a range of counterfactual scenarios, summarizing the information collected, and then packaging it for presentation. The authors show that each of these tasks raises important and challenging ethical questions. They comment on some of the philosophical issues involved in measuring health states, attributing causes to health outcomes, choosing the counterfactual against which to assess causes, aggregating and summarizing complex information across multiple domains, discounting, age-weighting, handling fetal deaths, measuring health inequalities, representing uncertainty, and assessing personal responsibility for health outcomes.

The IHME Covid-19 prediction model has been one of the most influential Covid models in the United States. Early on, it received heavy criticism for understating the extent of the epidemic. I argue that this criticism was based on a misunderstanding of the model. The model was best interpreted not as attempting to forecast the actual course of the epidemic. Rather, it was attempting to make a conditional projection: telling us how the epidemic would unfold, given certain assumptions. This misunderstanding of the IHME’s model prevented the public from seeing how dire the model’s projections actually were.

[OPEN ACCESS] Commentaries on the ethics of Covid lockdowns nearly all focus on offering substantive guidance to policy‐makers. Lockdowns, however, raise many ethical questions that admit of a range of reasonable answers. In such cases, policy‐making in a liberal democracy ought to be sensitive to which reasonable views the public actually holds—a topic existing bioethical work on lockdowns has not explored in detail. In this essay, I identify several important questions connected to the kind of influence the public ought to have on lockdown decision‐making, including how policy‐makers ought to handle misinformed or morally suspect viewpoints, and how policy‐makers ought to respond to minority viewpoints. I argue that questions like this, concerning the appropriate influence of the public on decision‐making, will be central to the field of bioethics as it increasingly focuses on policy and population‐level issues and therefore ought to be priorities for future work.

We sometimes wonder how health is distributed in our society. We may also want to know about the efficiency of different health programmes. Which public measures promise the greatest overall improvements in health? It can be hard to know how to go about answering questions like these, in large part because the varieties of ill health are heterogeneous, as are their consequences. The solution that health economists have long adopted is to appeal to summary or generic measures of health, such as the QALY, which attempt to put all types of ill health on the same scale. Daniel Hausman works from the assumption that we have reason to want a scalar measure like the QALY. He subjects existing techniques for generating such measures to a thorough critique, ultimately concluding that significant methodological changes are needed.