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There are a variety of reasons for which one might claim an exemption from a public health mandate such as a required COVID-19 vaccine. Good-faith exemption requests—for medical, religious, or other reasons—are generally recognized as legitimate and granted to individuals when the imposition of the mandate on the requestor is perceived to outweigh the corresponding risk their lack of vaccination poses to the health and rights of others. This paper develops a method of analysis rooted in Western analytic philosophy designed to examine these issues and arrive at a framework for assessing the scientific, moral, and religious claims for exemptions from COVID-19 vaccinations. I argue that some empirical and moral beliefs are epistemically superior to others when they have a correspondence with agreed-upon facts about the world, are grounded in shared human experience, employ strong and substantive reasons for their claims, and embrace common convictions evidenced in the character of moral agents. Such facts must be demonstrable in the form of observably verifiable evidence and reliable testimony. Only then should a request for an exemption to an otherwise-required public health mandate (including a vaccine) be recognized. The alternative creates various difficulties, including the problem of moral arbitrariness. [ABSTRACT FROM AUTHOR]

How moral decision-making occurs, matures over time and relates to behaviour is complex. To develop a full picture of moral decision-making, moral development and moral behaviour it is necessary to understand: (a) how real-time moral decisions are made (including relevant social and contextual factors), (b) what processes are required to develop to enable mature moral decisions, (c) how these processes develop over time, and (d) how moral decisions relate to behaviour. In this paper, psychological and social neuroscience theories of moral decision-making and development are briefly reviewed, as is the development of relevant component processes. Various component processes and factors are seen as required for moral decision-making and development, yet there is no comprehensive framework incorporating these components into one explanation of how real-time moral decisions are made and mature. In this paper, we integrated these components into a new framework based on social information processing (SIP) theory. Situational factors, and how both cognitive and affective processes guide moral decisions was incorporated into the Social Information Processing-Moral Decision-Making (SIP-MDM) framework, drawing upon theories and findings from developmental psychology and social neuroscience. How this framework goes beyond previous SIP models was outlined, followed by a discussion of how it can explain both real-time moral decisions and moral development. We concluded with how the SIP-MDM framework could be used to guide future research and theory development in this area. [ABSTRACT FROM AUTHOR]

Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare. An interdisciplinary literature search across four databases – PubMed, Scopus, Web of Science, and Arxiv – was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies. DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the 'moral' cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making. By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making. • Explores the potential of using machine learning for studying moral decision-making. • Using traditional choice models for moral choice analysis will likely lead to misleading insights. • Machine learning is gaining interest in the field of choice modelling. • Remains unclear how to combine both paradigms for studying moral decision-making. • A research agenda is being put forward for the field of health preference research. [ABSTRACT FROM AUTHOR]

This paper is predicated on the understanding that clinical encounters between clinicians and patients should be seen primarily as inter-relations among persons and, as such, are necessarily moral encounters. It aims to relocate the discussion to be had in challenging medical decision-making situations, including, for example, as the end of life comes into view, onto a more robust moral philosophical footing than is currently commonplace. In our contemporary era, those making moral decisions must be cognizant of the existence of perspectives other than their own, and be attuned to the demands of inter-subjectivity. Applicable to clinical practice, we propose and justify a Habermasian approach as one useful means of achieving what can be described as dialogic consensus. The Habermasian approach builds around, first, his discourse theory of morality as universalizable to all and, second, communicative action as a cooperative search for truth. It is a concrete way to ground the discourse which must be held in complex medical decision-making situations, in its actual reality. Considerations about the theoretical underpinnings of the application of dialogic consensus to clinical practice, and potential difficulties, are explored. [ABSTRACT FROM AUTHOR]

Abstract: Moral dilemma tasks have been a much appreciated experimental paradigm in empirical studies on moral cognition for decades and have, more recently, also become a preferred paradigm in the field of cognitive neuroscience of moral decision-making. Yet, studies using moral dilemmas suffer from two main shortcomings: they lack methodological homogeneity which impedes reliable comparisons of results across studies, thus making a metaanalysis manifestly impossible; and second, they overlook control of relevant design parameters. In this paper, we review from a principled standpoint the studies that use moral dilemmas to approach the psychology of moral judgment and its neural underpinnings. We present a systematic review of 19 experimental design parameters that can be identified in moral dilemmas. Accordingly, our analysis establishes a methodological basis for the required homogeneity between studies and suggests the consideration of experimental aspects that have not yet received much attention despite their relevance. [Copyright &y& Elsevier]