G. Owen Schaefer

We live in the Information Age. Advances over the past 50 years in computing technology have enabled ever-expanding capacity to generate, store, transfer, process and analyse information about people, societies, products, services, the environment—nearly every aspect of the world. In parallel, concerns over how such data is being used have emerged, focusing especially on issues of privacy and confidentiality. Yet as technological capabilities continue to expand, the debate over ethical uses of data inevitably has evolved as well. Whereas we used to just talk about data, now there is great concern over Big Data. This chapter explores what, exactly, Big Data is; the ethical ramifications of its emergence, particularly for consent and anonymization; and what sorts of policy solutions are being proposed to address these emerging challenges. We will not defend one particular approach, but hope this chapter can be a useful reference for those who are contemplating difficult questions surrounding responsible management of Big Data.

Precision medicine (PM) is an approach to research and healthcare delivery driven by insights from linked genomic, epigenetic, clinical, behavioural, and environmental data. Data sharing for PM, especially between parties with divergent strategic and commercial interests, requires balancing competing values, creating a trusted data ecosystem, and maintaining social license. This paper integrates findings from normative bioethics and empirical research in Singapore to discern recommendations for guidance on the ethical governance of genomic and health data. We distil findings from mixed methods research in Singapore and map these onto Kalkman et al.’s themes and principles for data sharing. Based on our normative and empirical analyses, we argue that the values of public benefit, fairness, accountability, and transparency are critical for maintaining social license for PM in Singapore. Consent is important but insufficient to secure public trust or support for data sharing if these other normative values are not achieved. Contrary to assertions in much of the existing bioethics literature on genomic and health data sharing, we found that different consent options (such as specific versus broad consent) had little impact on social license for data sharing. Our analysis provides empirically grounded, contextually appropriate, responsive normative guidance for trustworthy data governance in Singapore. While our findings are specific to Singapore's cultural and institutional context, our methodological approach and the relative de-emphasis of individual consent models offer important insights for other jurisdictions developing PM programs, particularly in non-Western settings where collective values may take precedence over individual autonomy.

Controlled Human Infection Models (CHIMs) are a type of clinical trial involving deliberately exposing human volunteers to an infectious agent. Compared to studies of natural infection, CHIMs offers distinctive benefits, from the ability to study presymptomatic infection to a direct assessment of the efficacy of vaccines and therapeutics in a shorter time and involving fewer participants. Although the CHIMs do not fundamentally differ from other early‐phase clinical trials, they raise a unique set of ethical considerations. Modern CHIMs have been limited to studies which pose very low risk of participants experiencing serious adverse events, and CHIMs which lie on the upper end of risk remain controversial. Setting risk thresholds for more hazardous trials has become a topic of debate leading to various attempts to develop a model to ethically evaluate these risks. While there are now well‐accepted practices around established pathogens, future pandemics may involve more extreme threats to public health and CHIMs may have an important emergency rescue function, with some groups willing to undertake significant risks. We must ask, what risk of death and serious morbidity should future CHIMs permit in the face of such challenges? This paper proposes a taxonomy laying out three possible approaches for thinking about risk thresholds in CHIM: (1) consistency; (2) engaging community perspectives; and (3) no risk threshold. We explore the strengths and weaknesses of each approach and offer a resource for those in the research community to inform their evaluation of the ethical permissibility of current and future CHIM.

All parties involved in researching, developing, manufacturing, and distributing COVID-19 vaccines need guidance on their ethical obligations. We focus on pharmaceutical companies' obligations because their capacities to research, develop, manufacture, and distribute vaccines make them uniquely placed for stemming the pandemic. We argue that an ethical approach to COVID-19 vaccine production and distribution should satisfy four uncontroversial principles: optimising vaccine production, including development, testing, and manufacturing; fair distribution; sustainability; and accountability. All parties' obligations should be coordinated and mutually consistent. For instance, companies should not be obligated to provide host countries with additional booster shots at the expense of fulfilling bilateral contracts with countries in which there are surges. Finally, any satisfactory approach should include mechanisms for assurance that all parties are honouring their obligations. This assurance enables countries, pharmaceutical companies, global organisations, and others to verify compliance with the chosen approach and protect ethically compliant stakeholders from being unfairly exploited by unethical behaviour of others.

COVID-19 vaccines are likely to be scarce for years to come. Many countries, from India to the U.K., have demonstrated vaccine nationalism. What are the ethical limits to this vaccine nationalism? Neither extreme nationalism nor extreme cosmopolitanism is ethically justifiable. Instead, we propose the fair priority for residents framework, in which governments can retain COVID-19 vaccine doses for their residents only to the extent that they are needed to maintain a noncrisis level of mortality while they are implementing reasonable public health interventions. Practically, a noncrisis level of mortality is that experienced during a bad influenza season, which society considers an acceptable background risk. Governments take action to limit mortality from influenza, but there is no emergency that includes severe lockdowns. This “flu-risk standard” is a nonarbitrary and generally accepted heuristic. Mortality above the flu-risk standard justifies greater governmental interventions, including retaining vaccines for a country's own citizens over global need. The precise level of vaccination needed to meet the flu-risk standard will depend upon empirical factors related to the pandemic. This links the ethical principles to the scientific data emerging from the emergency. Thus, the FPR framework recognizes that governments should prioritize procuring vaccines for their country when doing so is necessary to reduce mortality to noncrisis flu-like levels. But after that, a government is obligated to do its part to share vaccines to reduce risks of mortality for people in other countries. We consider and reject objections to the FPR framework based on a country: having developed a vaccine, raising taxes to pay for vaccine research and purchase, wanting to eliminate economic and social burdens, and being ineffective in combating COVID-19 through public health interventions.

Why is transparency important for the use of AI in healthcare? Responses to this question typically claim that transparency is something owed to the patient – because it is a condition for informed consent, legitimacy, accountability to the patient, etc. In this paper, we draw attention to why transparency can be valuable for medical practitioners. We claim that transparent AI models facilitate critical engagement by medical practitioners with AI models that they use. That is, they enable practitioners to assess why AI models make the recommendations they do, think about how those reasons affect their own beliefs and judgments, and make reasoned decisions about whether to maintain or change their own judgments. Via this process, AI models can help medical practitioners to improve their practice in a distinctly valuable way. In turn, this benefits both medical practitioners and their patients. This conclusion has important implications for AI design in healthcare: if AI models are to be used in healthcare, they should be designed in ways which allow medical practitioners to understand how the models arrive at their recommendations, and engage with them critically.

Why is transparency important for the use of AI in healthcare? Responses to this question typically claim that transparency is something owed to the patient – because it is a condition for informed consent, legitimacy, accountability to the patient, etc. In this paper, we draw attention to why transparency can be valuable for medical practitioners. We claim that transparent AI models facilitate _critical engagement_ by medical practitioners with AI models that they use. That is, they enable practitioners to assess why AI models make the recommendations they do, think about how those reasons affect their own beliefs and judgments, and make reasoned decisions about whether to maintain or change their own judgments. Via this process, AI models can help medical practitioners to improve their practice in a distinctly valuable way. In turn, this benefits both medical practitioners and their patients. This conclusion has important implications for AI design in healthcare: if AI models are to be used in healthcare, they should be designed in ways which allow medical practitioners to understand how the models arrive at their recommendations, and engage with them critically.

In a number of policy, institutional, activist and advocacy contexts, attributing bias to an algorithm does not just describe the algorithm but also imposes a particular, normatively laden conception of bias on others. Given the normative content of such bias attributions, this would involve making moral demands on others to rectify the algorithm, compensate the victims of such bias and/or not unselectively deploy the algorithm. It is also the case that moral demands, especially in the above‐mentioned contexts, are subject to a public justification requirement. As it turns out, the dominant accounts of bias in the literature presuppose some version of egalitarianism about justice and that any action that causally contributes to an unjust situation is itself wrong. Since these presuppositions are subject to reasonable disagreement, bias attributions in such situations are wrong because they violate the public justification requirement. In response, we develop a publicly justifiable conception of algorithmic bias.

Researchers and Institutional Review Board members often disagree about the permissibility of some parts or all of the former’s proposed research. At least sometimes, this disagreement is reasonable. This is often because there are competing values and no clear solution as to how to balance these values. Arguably, given that there are multiple reasonable ways in which these values might be balanced, IRBs are subject to an asymmetric public justification requirement. This requirement implies that IRBs should defer to researchers when the disagreement is reasonable. After briefly defending this claim, this paper examines its implications with respect to three questions that IRBs face. We argue that IRBs should not impose a strict limit on payments to participants, that they should take a more expansive view of what types of research are authorized by broad consent forms, and there should be no hard cap on risks participants might choose to undergo.

On extant accounts of trust, it is not apt to trust someone who seems or actually is untrustworthy. It also does not seem apt, on such accounts, to morally demand trust and blame people for not trusting. Yet, there seem to be cases where it is plausible that trusting the untrustworthy might be apt or where we might aptly demand trust and blame people for not trusting. In this paper, we sketch out an account of trust according to which to trust someone involves exercising a moral power which gives trustees permission or additional reasons to do as trusted. On this account, there can be moral reasons to trust other than the trustee’s reliability. This allows our account to better account for these cases than existing accounts of trust.

While philosophers are often concerned with the conditions for moral knowledge or justification, in practice something arguably less demanding is just as, if not more, important – reliably making correct moral judgments. Judges and juries should hand down fair sentences, government officials should decide on just laws, members of ethics committees should make sound recommendations, and so on. We want such agents, more often than not and as often as possible, to make the right decisions. The purpose of this paper is to propose a method of enhancing the moral reliability of such agents. In particular, we advocate for a procedural approach; certain internal processes generally contribute to people’s moral reliability. Building on the early work of Rawls, we identify several particular factors related to moral reasoning that are specific enough to be the target of practical intervention: logical competence, conceptual understanding, empirical competence, openness, empathy and bias. Improving on these processes can in turn make people more morally reliable in a variety of contexts and has implications for recent debates over moral enhancement.

Singapore is conducting its first controlled human infection (CHI) study, and is administering SARS-CoV-2 as the challenge agent. Ahead of this study, we conducted a survey to assess public perceptions in Singapore of CHI studies in general and with SARS-CoV-2, and the ethical issues they raise, including those around payments to research participants. Overall, there was large support for challenge studies in Singapore, suggesting they could obtain a social license. However, a minority strongly disagreed, and most respondents reported limited pre-survey knowledge about CHI studies. Importantly, Singaporeans support a higher incentive model of payment than is usually employed in challenge study research. They support either a Market Model or a Payment for Risk Model. There was most support for paying participants the highest rate offered—in our study, it was $SGD30 per hour. These results were broadly in line with a similar study in the UK, despite the latter having notably lower reported levels of public trust and, most recently, a highly criticized response to COVID-19. As such, general support for CHI studies may not be a direct function of background confidence in public or biomedical institutions but reflect other factors such as their intrinsic value and importance. More direct cross-cultural research in different contexts concerning attitudes towards CHI studies could help shed light on the extent that localized factors such as culture, history, and infrastructure affect both their acceptability and attitudes towards participant payment.

Recent developments in genetic technologies have provided prospective parents with increasing opportunities to influence their future child’s phenotype. This study aimed to understand public attitudes towards gene-based technologies and services, with a particular focus on improving educational outcomes. We conducted a cross-sectional survey among a Singaporean population (n=1438), adapting a survey instrument previously used in the US context. Our results suggested that Singaporeans had a greater moral acceptance of, and willingness to use, genetic technologies and services compared with the US population. Among the technologies examined, the use of polygenic embryo screening was considered more acceptable than gene editing. While these public attitudes show some support for the use of these technologies, further research and consultation among multiple stakeholder groups is necessary to determine whether such technology should be used and how it should be regulated.

Polygenic risk scores (PRSs) have recently been used to inform reproductive decision-making in the context of embryonic screening. While this is yet to be widespread, it is contested and raises several challenges. This article provides an overview of some of the ethical considerations that arise with using PRSs for embryo screening and offers a series of regulatory considerations for jurisdictions that may wish to permit this in the future. These regulatory considerations cover possible regulators and regulatory tools, eligibility criteria, information and education requirements and the need for ongoing refinement of the relevant technology, research and consultation.

This paper argues that one problem that besets black-box AI is that it lacks algorithmic justifiability. We argue that the norm of shared decision making in medical care presupposes that treatment decisions ought to be justifiable to the patient. Medical decisions are justifiable to the patient only if they are compatible with the patient’s values and preferences and the patient is able to see that this is so. Patient-directed justifiability is threatened by black-box AIs because the lack of rationale provided for the decision makes it difficult for patients to ascertain whether there is adequate fit between the decision and the patient’s values. This paper argues that achieving algorithmic transparency does not help patients bridge the gap between their medical decisions and values. We introduce a hypothetical model we call Justifiable AI to illustrate this argument. Justifiable AI aims at modelling normative and evaluative considerations in an explicit way so as to provide a stepping stone for patient and physician to jointly decide on a course of treatment. If our argument succeeds, we should prefer these justifiable models over alternatives if the former are available and aim to develop said models if not.

The development of some COVID-19 vaccines by private companies like Moderna and Sanofi-GSK has been substantially funded by various governments. While the Sanofi CEO has previously suggested that countries that fund this development ought to be given some priority, this suggestion has not been taken seriously in the literature. Considerations of nationalism, sustainability, need, and equitability have been more extensively discussed with respect to whether and how much a country is entitled to advance purchase orders of the vaccine under conditions of absolute scarcity. Yet, little attention has been paid to whether prior investment into developing a vaccine entitles a country to some priority with respect to these orders. Moreover, while not a majority view, some survey results show that a significant minority of the populace does endorse some view like this. This article argues that the minority have a point: recognizing funder countries some priority is justified by the weak Lockean claim (WLC). According to the WLC, the fact that someone has contributed to the development of something gives them some entitlement to the resultant product. This article will defend the WLC, and address objections to the argument, including those pertaining to questions of historical injustice and medical need. This argument does not imply an unconstrained entitlement. Rather, contribution to development is one morally relevant factor that must be tempered by and weighed against potentially more substantial claims to priority based on need, equity, and other considerations.

‘Advancing the public interest’ is a criterion for de-identified data use for research via several national data platforms and biobanks. This may be referred to via cognate terms such as public benefit, public good or social value. The criterion is often adopted without it being a legal requirement. It is a legal requirement in some jurisdictions for sharing identifiable data without consent, which does not apply to de-identified data. We argue that, even in circumstances where there are few or no legal restrictions on the sharing of de-identified data, there is a sound ethical reason for platforms to nevertheless impose a public interest criterion on data sharing. We argue that a public interest test is ethically essential for justifying research use of de-identified data via government-funded platforms because (1) it allows to promote public good and to minimise potential harmful consequences of research for both individuals and groups, for example, by offering grounds to reject research that could lead to stigmatisation of marginalised populations; (2) national data platforms hold public data and are made possible by government funds, and therefore should be used to support public interests and (3) it can demonstrate trustworthiness and contribute to promoting the social licence for data platforms to operate, which is especially important for efforts to align data governance policies with public norms and expectations.

What Is a Person: Untapped Insights from Africa, a new book by Nancy Jecker and Caesar Atuire, offers an appealing and fresh perspective into ongoing debates surrounding personhood.1 The bioethics literature has, largely, drawn from Western/global North thought or (as arguably with the case of principlism) amalgamations of those traditions.2 3 This leaves substantial room for enriching current debates through insights from and engagement with understudied traditions. Just such an approach is taken by Jecker and Atuire, who in particular bring African philosophical theories of personhood into dialogue with Western ones. Their critical appraisal ably identifies flaws with a range of theories across different traditions, and their book attempts to put forward a new theory of personhood that aims to draw from the best and discard the worst of those theories: Emergent Personhood. The book’s early sections indeed are very useful in terms of critical comparative analysis, resulting in insights such as the finding that both traditional African and traditional Western (or more specifically, Greco-Roman) thought actually overlap in terms of how personhood is conceptualised: extrinsic, earned, scalar and changing. Modern Western thought, though, eschewed those concepts first through the introduction of Christian moral equality, then Enlightenment systematic secular theories like deontology and utilitarianism that claim non-theological basis for such moral personhood. By contrast, the contemporary African theories Jecker and Atuire cite largely align with the traditional, relational accounts. So does their theory of Emergent Personhood succeed where African and Western thought have fallen short? While the theory’s contours are promising, several aspects are in need of further development. In this brief commentary, I will highlight three somewhat inter-related issues: (1) The criteria by which theories of personhood are evaluated are never given firm normative justification; (2) The normative force of Emergent Personhood’s relational foundations are not explicated; …

The integration of artificial intelligence (AI), particularly large language models (LLMs) like OpenAI’s ChatGPT, into clinical research could significantly enhance the informed consent process. This paper critically examines the ethical implications of employing LLMs to facilitate consent in clinical research. LLMs could offer considerable benefits, such as improving participant understanding and engagement, broadening participants’ access to the relevant information for informed consent and increasing the efficiency of consent procedures. However, these theoretical advantages are accompanied by ethical risks, including the potential for misinformation, coercion and challenges in accountability. Given the complex nature of consent in clinical research, which involves both written documentation (in the form of participant information sheets and informed consent forms) and in-person conversations with a researcher, the use of LLMs raises significant concerns about the adequacy of existing regulatory frameworks. Institutional Review Boards (IRBs) will need to consider substantial reforms to accommodate the integration of LLM-based consent processes. We explore five potential models for LLM implementation, ranging from supplementary roles to complete replacements of current consent processes, and offer recommendations for researchers and IRBs to navigate the ethical landscape. Thus, we aim to provide practical recommendations to facilitate the ethical introduction of LLM-based consent in research settings by considering factors such as participant understanding, information accuracy, human oversight and types of LLM applications in clinical research consent.

This commentary examines the role of sustainability in the latest draft of the WHO pandemic accord, highlighting its notable absence from the official list of guiding principles despite being mentioned frequently throughout the text. It argues that sustainability should be explicitly acknowledged as a core principle and given a clear definition tailored to pandemic preparedness, and proposes defining sustainability as ensuring that immediate emergency responses don't compromise future pandemic preparedness and response capabilities. Including sustainability as a guiding principle would serve two key purposes: validating the relevance of ethical principles in the accord's provisions and emphasizing the importance of long-term planning in pandemic preparedness. The commentary notes that while humans naturally discount future consequences, sustainable pandemic planning requires considering impacts beyond the next pandemic. It recommends coupling any reforms, such as intellectual property changes, with alternative innovation incentives to maintain a balanced approach to pandemic preparedness and response.

Monitored Emergency Use of Unregistered and Experimental Interventions (MEURI) is an ethical framework developed by the WHO for using unproven interventions in public health emergencies outside the context of medical research. It is mainly intended for use when medical research would be impracticable, but there is still a need to systematically gather data about unproven interventions. As such, it is designed as something of a middle ground between clinical and research ethical frameworks. However, I argue that MEURI does not truly lie at the intersection of clinical care and research. Due to its intent, structure and oversight requirements, it takes on most of the crucial features of research, to the point that it is best understood as a form of research. As a result, cases where MEURI could practicably be applied should instead make use of existing research frameworks. For those circumstances where research is truly impracticable, a more straightforward oversight system than MEURI is needed. While existing practices of compassionate use have some applicability, proposals to make use of clinical ethics committees to oversee unproven interventions may help achieve the right balance in acting in a patient’s best interests when the relevant evidence base is weak.

The COVID-19 pandemic revealed numerous weaknesses in pandemic preparedness and response, including underfunding, inadequate surveillance, and inequitable distribution of countermeasures. To overcome these weaknesses for future pandemics, WHO released a zero draft of a pandemic treaty in February, 2023, and subsequently a revised bureau's text in May, 2023. COVID-19 made clear that pandemic prevention, preparedness, and response reflect choices and value judgements. These decisions are therefore not a purely scientific or technical exercise, but are fundamentally grounded in ethics. The latest treaty draft reflects these ethical considerations by including a section entitled Guiding Principles and Approaches. Most of these principles are ethical—they establish core values that undergird the treaty. Unfortunately, the treaty draft's set of principles are numerous, overlapping, and show inadequate coherence and consistency. We propose two improvements to this section of the draft pandemic treaty. First, key guiding ethical principles should be clearer and more precise than they currently are. Second, the link between ethical principles and policy implementation should be clearly established and define boundaries on acceptable interpretation, ensuring that signatories abide by these principles.

Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens’ jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme.

With the world grappling with continued spread of monkeypox internationally, vaccines play a crucial role in mitigating the harms from infection and preventing spread. However, countries with the greatest need - particularly historically endemic countries with the highest monkeypox case-fatality rates - are not able to acquire scarce vaccines. This is unjust, and requires rectification through equitable allocation of vaccines globally. We propose applying the Fair Priority Model for such allocation, which emphasizes three key principles: 1) preventing harm; 2) prioritizing the disadvantaged; and 3) treating people with equal moral concern. Post-exposure prophylaxis (PEPV) has the most potential to mitigate harm, and so ensuring countries have sufficient supply for PEPV should be the first priority. And historically endemic countries, which face disadvantages that compound potential harms from monkeypox, should be the first recipients of such vaccines. Once sufficient supply is allocated for countries to apply PEPV, global allocation could move on to pre-exposure prophylaxis (PrEP), again prioritizing historically endemic countries first before distribution to the rest of the global community, based on projected number of cases and vulnerability to harm.

Monitored Emergency Use of Unregistered and Experimental Interventions (MEURI) is an ethical framework developed by the WHO for using unproven interventions in public health emergencies outside the context of medical research. It is mainly intended for use when medical research would be impracticable, but there is still a need to systematically gather data about unproven interventions. As such, it is designed as something of a middle ground between clinical and research ethical frameworks. However, I argue that MEURI does not truly lie at the intersection of clinical care and research. Due to its intent, structure and oversight requirements, it takes on most of the crucial features of research, to the point that it is best understood as a form of research. As a result, cases where MEURI could practicably be applied should instead make use of existing research frameworks. For those circumstances where research is truly impracticable, a more straightforward oversight system than MEURI is needed. While existing practices of compassionate use have some applicability, proposals to make use of clinical ethics committees to oversee unproven interventions may help achieve the right balance in acting in a patient’s best interests when the relevant evidence base is weak.

Countermeasures for mpox (formerly known as monkeypox), primarily vaccines, have been in limited supply in many countries during outbreaks. Equitable allocation of scarce resources during public health emergencies is a complex challenge. Identifying the objectives and core values for the allocation of mpox countermeasures, using those values to provide guidance for priority groups and prioritisation tiers, and optimising allocation implementation are important. The fundamental values for the allocation of mpox countermeasures are: preventing death and illness; reducing the association between death or illness and unjust disparities; prioritising those who prevent harm or mitigate disparities; recognising contributions to combating an outbreak; and treating similar individuals similarly. Ethically and equitably marshalling available countermeasures requires articulating these fundamental objectives, identifying priority tiers, and recognising trade-offs between prioritising the people at the highest risk of infection and the people at the highest risk of harm if infected. These five values can provide guidance on preferable priority categories for a more ethically sound response and suggest methods for optimising allocation of countermeasures for mpox and other diseases for which countermeasures are in short supply. Properly marshalling available countermeasures will be crucial for future effective and equitable national responses to outbreaks.