Suchergebnisse

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Acknowledgments -- List of Acronyms -- 1 Introduction -- PART I: BRAINs in Battle -- 2 Predicting the Future -- 3 The Science of Persuasion -- 4 Building a Better Warfighter -- 5 Neuroweapons -- PART II: Neuroethics and National Security -- 6 Whither Neuroethics? -- 7 Translation -- 8 Dual-Use -- 9 Corruption -- 10 Neurosupremacy -- PART III: Policy -- 11 Self-Regulation -- 12 Organizations -- 13 Nations -- 14 Global Governance -- 15 Restructuring Science -- List of References -- Appendix: A working bibliography of neuroethics and national security -- Index.

Human infection challenge studies involving the intentional infection of research participants with a disease-causing agent have recently been suggested as a means to speed up the search for a vaccine for the ongoing coronavirus disease 2019 (COVID-19) outbreak. Calls for challenge studies, however, rely on the expected social value of these studies. This value represents more than the simple possibility that a successful study will lead to the rapid development and dissemination of vaccines but also some expectation that this will actually occur. I show how this expectation may not be realistic in the current political moment and offer potential ways to make sure that any challenge trials that arise actually achieve their goals.

Existing ethical discussion considers the differences in care for identified versus statistical lives. However, there has been little attention to the different degrees of care that are taken for different kinds of statistical lives. Here we argue that for a given number of statistical lives at stake, there will sometimes be different, and usually greater, care taken to protect predictable statistical lives, in which the number of lives that will be lost can be predicted fairly accurately, than for unpredictable statistical lives, where the lives are at stake because of a low‐probability event, such that most likely no one will be affected by the decision but with low probability some lives will be at stake. One reason for this difference is the statistical challenge of estimating low probabilities, and in particular the tendency of common approaches to underestimate these probabilities. Another is the existence of rational incentives to treat unpredictable risks as if the probabilities were lower than they are. Some of these factors apply outside the pure economic context, to institutions, individuals, and governments. We argue that there is no ethical reason to treat unpredictable statistical lives differently from predictable statistical lives. Moreover, lives that are unpredictable from the perspective of an individual agent may become predictable when aggregated to the level of a societal decision. Underprotection of unpredictable statistical lives is a form of market failure that may need to be corrected by altering regulation, introducing compulsory liability insurance, or other social policies.

Existing ethical discussion considers the differences in care for identified versus statistical lives. However there has been little attention to the different degrees of care that are taken for different kinds of statistical lives. Here we argue that for a given number of statistical lives at stake, there will sometimes be different, and usually greater care taken to protect predictable statistical lives, in which the number of lives that will be lost can be predicted fairly accurately, than for unpredictable statistical lives, where the lives are at stake because of a low-probability event, such that most likely no one will be affected by the decision but with low probability some lives will be at stake. One reason for this difference is the statistical challenge of estimating low probabilities, and in particular the tendency of common approaches to underestimate these probabilities. Another is the existence of rational incentives to treat unpredictable risks as if the probabilities were lower than they are. Some of these factors apply outside the pure economic context, to institutions, individuals, and governments. We argue that there is no ethical reason to treat unpredictable statistical lives differently from predictable statistical lives. Moreover, lives that are unpredictable from the perspective of an individual agent may become predictable when aggregated to the level of a societal decision. Underprotection of unpredictable statistical lives is a form of market failure that may need to be corrected by altering regulation, introducing compulsory liability insurance, or other social policies.

BACKGROUND: Individuals with chronic disease may be at higher risk of dying from COVID-19, yet no association has been established between chronic illness and COVID-19 risk perception, engagement with nonpharmaceutical interventions (NPIs), or vaccine acceptance. METHODS: We surveyed US residents who self-reported a chronic respiratory or autoimmune disease in February 2021. Respondents reported beliefs about the risk of COVID-19 to personal and public health, adoption and support of NPIs, willingness to be vaccinated against COVID-19, and reasons for vaccination willingness. We evaluated the association between disease status and COVID-19 behaviors or attitudes, adjusting for demographic and political factors. RESULTS: Compared to healthy controls, chronic disease was associated with increased belief that COVID-19 was a personal (Respiratory = 0.12, 95% confidence interval (CI) = 0.10 - 0.15; Autoimmune = 0.11, CI = 0.08 - 0.14) and public threat (Respiratory = 0.04, CI = 0.02 - 0.06; Autoimmune = 0.03, CI = 0.01 - 0.06), and support for NPIs. Chronic respiratory disease was associated with willingness to be vaccinated (0.6, CI = 0.05 - 0.09). Personal protection was associated with vaccination (Respiratory = 1.08, CI = 1.03 – 1.13; Autoimmune = 1.06, CI = 1.01 – 1.11). Autoimmune disease was associated with fear of a bad vaccine reaction (1.22, CI = 1.06 - 1.41) among those unwilling to be vaccinated. CONCLUSIONS: In the US, chronic disease status is significantly related to risk perceptions of COVID, support of personal and community risk mitigation measures, and willingness to be vaccinated.

Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.

Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.