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An argument for the centrality of rights in health security, and how to apply ethical principles to protecting those rights during public health crises. In recent years, efforts to respond to infectious diseases have been described in terms of national and global security, leading to the formation of the field of “health security.” In War on All Fronts, Nicholas G. Evans provides a novel theory of just health securityand its relation to the practice of conventional public health. Using COVID-19 as a jumping-off point to examine wider issues, including how the US thinks about and prepares for pandemics, Evans shows the flaws in using the “war metaphor" and how any serious understanding of health security must square with human rights—even when a disease poses a threat to national security. Evans asks what ethical principles justify declaring, and taking action during, a public health emergency such as the ongoing COVID-19 pandemic. The relevant principles, he argues, parallel those of the ethics of armed conflict. Just war theory, properly understood, begins with pacifism and a commitment to the right not to be killed and then steps back to ask under what limited conditions it is permissible to kill. In a similar way, a just health security must also begin with the idea that public health should hold human rights sacrosanct and then ask under what limited conditions other concerns might prevail. Evans's overall goal is to formulate a guide to action, particularly as the world deals with the fallout of the COVID-19 pandemic. Turning to the transition from war back to peace in public health, he looks at reparation, rebuilding, and the accountability of actors during the crisis.

Background COVID-19 disproportionately affects those with preexisting conditions, but little research has determined whether those with chronic diseases view the pandemic itself differently - and whether there are differences between chronic diseases. We theorized that while individuals with respiratory disease or autoimmune disorders would perceive greater threat from COVID-19 and be more supportive of non-pharmaceutical interventions (NPIs), those with autoimmune disorders would be less likely to support vaccination-based interventions. Methods We conducted a two-wave online survey conducted in February and November 2021 asking respondents their beliefs about COVID-19 risk perception, adoption and support of interventions, willingness to be vaccinated against COVID-19, and reasons for vaccination. Regression analysis was conducted to assess the relationship of respondents reporting a chronic disease and COVID-19 behaviors and attitudes, compared to healthy respondents adjusting for demographic and political factors. Results In the initial survey, individuals reporting a chronic disease had both stronger feelings of risk from COVID-19 as well as preferences for NPIs than healthy controls. The only NPI that was still practiced significantly more compared to healthy controls in the resample was limiting trips outside of the home. Support for community-level NPIs was higher among individuals reporting a chronic disease than healthy controls and remained high among those with respiratory diseases in sample 2. Vaccine acceptance produced more divergent results: those reporting chronic respiratory diseases were 6% more willing to be vaccinated than healthy controls, while we found no significant difference between individuals with autoimmune diseases and healthy controls. Respondents with chronic respiratory disease and those with autoimmune diseases were more likely to want to be vaccinated to protect themselves from COVID-19, and those with an autoimmune disease were more likely to report fear of a bad vaccine reaction as the reason for vaccine hesitancy. In the resample, neither those with respiratory diseases nor autoimmune diseases reported being more willing to receive a booster vaccine than healthy controls. Conclusions It is not enough to recognize the importance of health in determining attitudes: nuanced differences between conditions must also be recognized.

This chapter addresses a notorious ethical issue concerning the military use of artificial intelligence (AI), which may represent the most disruptive and ethically problematic emerging technology on the horizon. The chapter argues that while civilians are never spared in war, part of the process of war is the moral cost-sharing of this burden for killing. AI would subvert this burden in unjust ways if applied for lethal autonomous weapon systems (LAWS). Because LAWS lack moral agency, in particular the capacity for moral emotions, moral costs are only borne by the dead and their loved ones. The chapter argues that it is unjust insofar as those responsible for unjust harm to others ought to share those costs by outlining and applying the Moral Affect Principle. This presents a dilemma: either autonomous weaponry are designed to be capable of moral emotions, which may be a step too far even for proponents of LAWS, or the use of such weaponry needs to be limited, which may risk worsening combatant casualties and achieving strategic aims.

In debates over the regulation of communication related to dual-use research, the risks that such communication creates must be weighed against against the value of scientific autonomy. The censorship of such communication seems justifiable in certain cases, given the potentially catastrophic applications of some dual-use research. This conclusion however, gives rise to another kind of danger: that regulators will use overly simplistic cost-benefit analysis to rationalize excessive regulation of scientific research. In response to this, we show how institutional design principles and normative frameworks from free speech theory can be used to help extend the argument for regulating dangerous dual-use research beyond overly simplistic cost-benefit reasoning, but without reverting to an implausibly absolutist view of scientific autonomy.

The study of how to build ethical autonomous vehicles (AVs) initially focused on the trolley problem and on questions about who to harm, but it has evolved to consider how to distribute the risk of harm. This chapter examines that evolution and argue that it signals a change in the kinds of philosophical, metaphilosophical, and empirical tools that should be marshalled to solve pressing ethical issues in AVs. We show this by examining cases in which AVs are required to make judgments under conditions of moral uncertainty, and when thinking about the widespread distribution of risks and benefits from AVs.

Background Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Methods Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Results Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Conclusions Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

So-called ‘gain-of-function’ (GOF) research is virological research that results in a virus substantially more virulent or transmissible than its wild antecedent. GOF research has been subject to ethical analysis in the past, but the methods of GOF research have to date been underexamined by philosophers in these analyses. Here, we examine the typical animal used in influenza GOF experiments, the ferret, and show how despite its longstanding use, it does not easily satisfy the desirable criteria for ananimal model. We then discuss the limitations of the ferret model, and how those epistemic limitations bear on ethical and policy questions around the risks and benefits of GOF research. We conclude with a reflection on how philosophy of science can contribute to ethical and policy debates around the risks, benefits and relative priority of life sciences research.