More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this (...) the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions. (shrink)

Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite research that integrates large amounts of personal health data, especially genomic and genetic data, might contribute to a more personalized medicine. This type of research requires the transfer and storage of highly sensitive data, which raises the question of how to protect data subjects against data harm, such as privacy breach, disempowerment, disenfranchisement, and exploitation. As a result, there is a trade-off between reaping the benefits (...) of big-data-based biomedical research and protecting data subjects’ right to informational privacy. Arguments Blockchain technologies are often discussed as a technical fix for the abovementioned trade-off due to their specific features, namely data provenance, decentralization, immutability, and access and governance system. However, implementing blockchain technologies in biomedical research also raises questions regarding consent, legal frameworks, and workflow integration. Hence, accompanying measures, which I call enablers, are necessary to unleash the potential of blockchain technologies. These enablers are innovative models of consent, data ownership models, and regulatory models. Conclusion Blockchain technologies as a technical fix alone is insufficient to resolve the aforementioned trade-off. Combining this technical fix with the enablers outlined above might be the best way to perform biomedical research based on big data and at the same time protect the informational privacy of data subjects. (shrink)

Este artículo explora las consideraciones éticas que rodean el uso de la Inteligencia Artificial (IA) en la academia. Se establecen principios éticos generales para la IA en este ámbito, como la transparencia, la equidad, la responsabilidad, la privacidad y la integridad académica. En cuanto a la educación asistida por IA, se enfatiza la importancia de la accesibilidad, la no discriminación y la evaluación crítica de los resultados. Se recomienda que la IA se use para complementar y no para reemplazar la (...) enseñanza tradicional. En cuanto al ámbito de la investigación, se hace énfasis en la necesidad de garantizar la calidad y la veracidad de los datos utilizados, así como la transparencia en los métodos y resultados. Se deben considerar los posibles sesgos y riesgos de la IA en la investigación. El artículo concluye que la adopción responsable de la IA en la academia requiere una reflexión crítica sobre sus implicaciones éticas. Se recomienda la creación de marcos éticos específicos para cada contexto y la participación de diversos actores en la toma de decisiones. (shrink)

La noticia de la conclusión del primer pangenoma humano ocurre veinte años después de que se pudo contar con una versión de referencia de la información genética completa de la especie humana. Las limitaciones técnicas de ese tiempo permitieron que esa versión tuviera errores y varias lagunas de la información genética. Ahora es posible contar con un nuevo atlas gigante con información que permite evidenciar la gran diversidad genética de la especie humana. Este trabajo está siendo realizado por el Consorcio (...) Internacional de Referencia del Pangenoma Humano, del que hacen parte cerca de cien investigadores y está financiado por el Instituto Nacional de Investigación del Genoma Humano y por los Institutos Nacionales de Salud de EEUU. En este escrito que describe a grandes rasgos en qué consiste el proyecto, cuáles son sus aplicaciones posibles y las dificultades técnicas que tiene actualmente. Se repasan también algunos aspectos éticos y jurídicos y se dan pautas para su culminación: profundizar en la reflexión ética de los problemas que se generan; cuidar la formación en ética de la investigación, bioética e integridad científica en todos los actores del proceso; no perder de vista la Declaración Universal sobre el genoma humano y los derechos humanos de la UNESCO; orientar los desarrollos de la bioinformática, para que los datos obtenidos se manejen de una mejor manera; extremar la prudencia en el manejo de esos datos; revisar y reajustar las prioridades que se tienen en la investigación de estas temáticas. (shrink)

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and (...) ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR. (shrink)

BackgroundThe ability of healthcare to protect sensitive personal data in medical records and registers might influence public trust, which in turn might influence willingness to allow healthcare to use such data. The aim of this study was to examine how the general public’s trust relates to their attitudes towards uses of health data.MethodsA stratified sample from the general Swedish population received a questionnaire about their willingness to share health data. Respondents were also asked about their trust in the management and (...) protection of electronic health data.ResultsA large majority (81.9%) of respondents revealed high levels of trust in the ability of healthcare to protect electronic patient data. Good health was associated with significantly higher levels of trust compared to bad health. Respondents with low levels of trust were significantly less willing to allow personal data to be used for different purposes and were more inclined to insist on being asked for permission beforehand. Those with low levels of trust also perceived risks of unauthorized access to personal data to be higher and the likely damage of such unauthorized access worse, compared to those with high levels of trust.ConclusionsTrust in the ability of healthcare to protect electronic health is generally high in Sweden. Those with higher levels of trust are more willing to let their data be used, including without informed consent. It thus seems crucial to promote trust in order to be able to reap the benefits that digitalization makes possible through increased access and use of data in healthcare. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are in tension with a (...) prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (shrink)

Although existing work draws attention to a range of obstacles in realizing fair AI, the field lacks an account that emphasizes how these worries hang together in a systematic way. Furthermore, a review of the fair AI and philosophical literature demonstrates the unsuitability of ‘treat like cases alike’ and other intuitive notions as conceptions of fairness. That review then generates three desiderata for a replacement conception of fairness valuable to AI research: (1) It must provide a meta-theory for understanding tradeoffs, (...) entailing that it must be flexible enough to capture diverse species of objection to decisions. (2) It must not appeal to an impartial perspective (neutral data, objective data, or final arbiter.) (3) It must foreground the way in which judgments of fairness are sensitive to context, i.e., to historical and institutional states of affairs. We argue that a conception of fairness as appropriate concession in the historical iteration of institutional decisions meets these three desiderata. On the basis of this definition, we organize the insights of commentators into a process-structure map of the ethical territory that we hope will bring clarity to computer scientists and ethicists analyzing Fair AI while clearing some ground for further technical and philosophical work. (shrink)

Essentialists understand authenticity as an inherent quality of a person, object, artifact, or place, whereas constructionists consider authenticity as a social creation without any pre-given essence, factuality, or reality. In this paper, we move beyond the essentialist-constructionist dichotomy. Rather than focusing on the question whether authenticity can be found or needs to be constructed, we hook into the idea that authenticity is an interactive, culturally informed process of negotiation. In addition to essentialist and constructionist approaches, we discuss a third, less (...) well-known approach that cannot be reduced to any of the two forms. This approach celebrates the authenticity of inauthenticity by amplifying the made. We argue that the value of authenticity lies not in choosing for one of these approaches, but in the degree to which the process of negotiating authenticity enables a critical formation of selves and societies. Authenticity is often invoked as a method of social control or a mark of power relations: once something is defined as authentic, it is no longer questioned. Emerging technologies—especially data-driven technologies—have the capacity to conceal these power relations, propel a shift in power, and dominate authentication processes. This raises the question how processes of authentication can contribute to a critical formation of selves and societies, against the backdrop of emerging technologies. We argue in favor of an interactionist approach of authenticity and discuss the importance of creating space in authentication processes that are increasingly influenced by technology as an invisible actor. (shrink)

Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To (...) reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating (...) this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

Background The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. Objectives We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical (...) ways. Methods We conducted focus groups and interviews in 2019 with 39 big data stakeholders who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. Results Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide, enact shared data governance, cultivate public trust and earn social license for big data uses, and refocus ethical approaches. Conclusions Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. (shrink)

This paper develops a philosophical account of moral disruption. According to Robert Baker, moral disruption is a process in which technological innovations undermine established moral norms without clearly leading to a new set of norms. Here I analyze this process in terms of moral uncertainty, formulating a philosophical account with two variants. On the harm account, such uncertainty is always harmful because it blocks our knowledge of our own and others’ moral obligations. On the qualified harm account, there is no (...) harm in cases where moral uncertainty is related to innovation that is “for the best” in historical perspective or where uncertainty is the expression of a deliberative virtue. The two accounts are compared by applying them to Baker’s historical case of the introduction of mechanical ventilation and organ transplantation technologies, as well as the present-day case of mass data practices in the health domain. (shrink)

This paper develops a philosophical account of moral disruption. According to Robert Baker (2013), moral disruption is a process in which technological innovations undermine established moral norms without clearly leading to a new set of norms. Here I analyze this process in terms of moral uncertainty, formulating a philosophical account with two variants. On the Harm Account, such uncertainty is always harmful because it blocks our knowledge of our own and others’ moral obligations. On the Qualified Harm Account, there is (...) no harm in cases where moral uncertainty is related to innovation that is “for the best” in historical perspective, or where uncertainty is the expression of a deliberative virtue. The two accounts are compared by applying them to Baker’s historical case of the introduction of mechanical ventilation and organ transplantation technologies, as well as the present-day case of mass data practices in the health domain. (shrink)

In this panel, we explore the future of value sensitive design (VSD). The stakes are high. Many in public and private sectors and in civil society are gradually realizing that taking our values seriously implies that we have to ensure that values effectively inform the design of technology which, in turn, shapes people’s lives. Value sensitive design offers a highly developed set of theory, tools, and methods to systematically do so.

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and used (...) by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, (...) knowledge, and capacity, and reducing hazard. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. As such, for some studies the Institutional (...) Review Board/Research Ethics Committee review process should not assess the practicality of gaining consent for data use. Instead the review process should focus on assessing the public good of the research, public engagement and transparency. (shrink)

As the recent inaugural Ethical, Legal, and Social Issues 2.0 conference made clear, the effects of information communication technology are pervasive in biomedical research. Data initiatives are arising in all corners of biomedicine. Data sharing efforts already promised to surpass even the ambitious goals of the National Human Genome Research Institute, only 5 years after publication of its 10-year vision. ELSI research was established, in part, to address challenges of open data access and data sharing. However, by and large, ELSI (...) research projects address particular concerns of a given population, jurisdiction, type of research practice or type of data. This does not necessarily facilitate coherent data policy for sustainable data stewardship. Forward-looking, data friendly strategies need to be considered. Orchestration strategies are needed which overcome barriers to collective action. Here we present challenges policymakers face, and suggest three basics steps towards meeting them. First, policymakers must recognise the systematic change that occurs when ICT enables dataflow itself to become an organising principle of biomedical research. Second, methods for identifying and gathering types of metadata suitable for ELSI research ought to be developed and regulated. Third, policymakers need to organise in ways that mirror the new vision for data-enabled research that data technologies are making possible, as ELSI 2.0 encourages researchers to do. Taking these steps will help ensure research evolves in ways that warrants trust of the public while still supporting widespread ethical access to necessary data, research subjects, samples and findings. (shrink)

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be (...) rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols. (shrink)

The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many (...) benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by Computer and Information (...) Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (shrink)

Definition of the problem Medical ethics is increasingly faced with issues that result from power asymmetries and epistemic injustice. However, medical ethics lacks the epistemic lenses for analyzing these social context factors of clinical practice. A theoretical and conceptual reconfiguration is necessary in order to be able to address these issues. Arguments This paper discusses the foundations of critical medical ethics, which takes perspectives and epistemic categories from critical theories. This includes the Critical Theory of the Frankfurt School as well (...) as those approaches from social sciences and cultural studies that reflect on power asymmetries. By drawing on these theories, the epistemic spectrum of medical ethics could be broadened in order to better include real power relations into normative analyses. The paper starts by outlining the contribution of social sciences to empirical medical ethics. This shows that an empirical approach as such cannot bridge the epistemic gap. In a further step, epistemic lenses and concepts from critical theories are discussed, such as Critical Theory of the Frankfurt School, feminist approaches, postcolonial approaches, queer theory, gender studies, and science and technology studies (STS). The conclusion sums up the foundations of critical medical ethics and discusses its limitations. Conclusion Critical medical ethics possesses the epistemic categories and broader social perspective needed to address issues in medicine that result from power asymmetries and epistemic injustice in society. (shrink)

In this article, we use the theory of Information Ethics to argue that deceased people have a prima facie moral right to privacy in the context of health data research, and that this should be reflected in regulation and guidelines. After death, people are no longer biological subjects but continue to exist as informational entities which can still be harmed/damaged. We find that while the instrumental value of recognising post-mortem privacy lies in the preservation of the social contract for health (...) research, its intrinsic value is grounded in respect for the dignity of the post-mortem informational entity. However, existing guidance on post-mortem data protection is available only in the context of genetic studies. In comparing the characteristics of genetic data and other health-related data, we identify two features of DNA often given as arguments for this genetic exceptionalism: relationality and embodiment. We use these concepts to show that at the appropriate Level of Abstraction, there is no morally relevant distinction between posthumous genetic and other health data. Thus, genetic data should not automatically receive special moral status after death. Instead we make a plea for ‘contextual exceptionalism’. Our analysis concludes by reflecting on a real-world case and providing suggestions for contextual factors that researchers and oversight bodies should take into account when designing and evaluating research projects with health data from deceased subjects. (shrink)

Background Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance. -/- Methods We conducted qualitative, semi-structured interviews with 24 people living with HIV who had (...) been previously incarcerated in county jails about their perspectives on and experiences with HIV surveillance, as part of a larger study to characterize ethical considerations in leveraging big data techniques to enhance continuity of care for incarcerated people living with HIV. -/- Results Most participants expressed support for the state health department tracking HIV testing results and viral load data. Several viewed HIV surveillance as a violation of privacy, and several had actively avoided contact from state public health outreach workers. Participants were most likely to express reservations about surveillance when they viewed the state’s motives as self-interested. Perspectives highlight the mistrust that structurally vulnerable people may have in the state’s capacity to act as an agent of welfare. Findings suggest that adopting a nuanced, context-sensitive view on surveillance is essential. -/- Conclusions Establishing trustworthiness through interpersonal interactions with public health personnel is important to reversing historical legacies of harm to racial minorities and structurally vulnerable groups. Empowering stakeholders to participate in the design and implementation of data infrastructure and governance is critical for advancing a data justice agenda, and can offset privacy concerns. The next steps in advancing the data justice framework in public health surveillance will be to innovate ways to represent the voices of structurally vulnerable groups in the design and governance of big data initiatives. (shrink)

The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede (...) informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. (shrink)

In this paper we argue that ‘informed’ consent in Big Data genomic biobanking is frequently less than optimally informative. This is due to the particular features of genomic biobanking research which render it ethically problematic. We discuss these features together with details of consent models aimed to address them. Using insights from consent theory, we provide a detailed analysis of the essential components of informed consent which includes recommendations to improve consent performance. In addition, and using insights from philosophy of (...) mind and language and psycholinguistics we support our analyses by identifying the nature and function of concepts (ideas) operational in human cognition and language together with an implicit coding/decoding model of human communication. We identify this model as the source of patients/participants poor understanding. We suggest an alternative, explicit model of human communication, namely, that of relevance‐theoretic inference which obviates the limitations of the code model. We suggest practical strategies to assist health service professionals to ensure that the specific information they provide concerning the proposed treatment or research is used to inform participants’ decision to consent. We do not prescribe a standard, formal approach to decision‐making where boxes are ticked; rather, we aim to focus attention towards the sorts of considerations and questions that might usefully be borne in mind in any consent situation. We hope that our theorising will be of real practical benefit to nurses and midwives working on the clinical and research front‐line of genomic science. (shrink)

We have investigated attitudes towards the use of health data among the Swedish population by analyzing data from a survey answered by 1645 persons. Health data are potentially useful for a variety of purposes. Yet information about health remains sensitive. A balance therefore has to be struck between these opposing considerations in a number of contexts. The attitudes among those whose data is concerned will influence the perceived legitimacy of policies regulating health data use. We aimed to investigate what views (...) are held by the general public, and what aspects matter for the willingness to let one’s data be used not only for one’s own care but also for other purposes. We found that while there is a broad willingness to let one’s data be used, the possibility to influence that use is considered important. The study also indicated that when respondents are required to balance different interests, priority is typically given to compulsory schemes ensuring that data are available where needed, rather than voluntary participation and data protection. The policy implications to be drawn from this are not self-evident, however, since the fact that a majority has a certain attitude does not by itself determine the most adequate policy. (shrink)

In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digital data and information commons as a way to counteract this asymmetry. In this paper I raise two concerns with this argument: First, because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily transposed to digital commons. Second, not (...) all data and information commons are suitable to address power differentials. In order to create digital commons that effectively address power asymmetries we must pay more systematic attention to the issue of exclusion from digital data and information commons. Why and how digital data and information commons exclude, and what the consequences of such exclusion are, decide whether commons can change power asymmetries or whether they are more likely to perpetuate them. (shrink)

This paper draws on the emerging field of innovation ethics to complement the more established field of responsible innovation by focusing on key ethical issues raised by technological innovations. One key limitation of influential frameworks of RI is that they tend to neglect some key ethical issues raised by innovation, as well as major normative dimensions of the notion of responsibility. We explain how IE could enrich RI by stressing the more important role that ethical analysis should play in RI. (...) We focus on two transversal issues of IE: the issue of redrawing conceptual boundaries, especially the topic of the artificialization of the world, and the issue of responsibility, especially the notion of total responsibility. We address these two issues from the thematic perspective of IE, thereby generating lessons learnt for RI. These two examples are taken as illustrations and blueprint of the dialogue that should take place between the two fields. (shrink)

This paper addresses a problem that has so far been neglected by scholars investigating the ethics of Big Data and policy makers: that is the ethical implications of Big Data initiatives’ environmental impact. Building on literature in environmental studies, cultural studies and Science and Technology Studies, the article draws attention to the physical presence of data, the material configuration of digital service, and the space occupied by data. It then explains how this material and situated character of data raises questions (...) concerning the ethics of the increasingly fashionable Big Data discourses. It argues that attention should be paid to the vocabulary currently used when discussing the governance of data initiatives; the internal tension between current data initiatives and environmental policies; issues of fair distribution. The article explains how taking into account these aspects would allow for a more responsible behaviour in the context of data storage and production. (shrink)

A growing literature is taking an institutionalist and governance perspective on how algorithms shape society based on unprecedented capacities for managing social complexity. Algorithmic governance altogether emerges as a novel and distinctive kind of societal steering. It appears to transcend established categories and modes of governance—and thus seems to call for new ways of thinking about how social relations can be regulated and ordered. However, as this paper argues, despite its novel way of realizing outcomes of collective steering and coordination, (...) it can nevertheless be grasped with an old and fundamental figure in political philosophy: that of Thomas Hobbes’ Leviathan. Comparing algorithmic governance with this figure serves to highlight their similarities as socio-political arrangements, and specifically to clarify how algorithmic governance parallels the apolitical traits of the Leviathan—it eliminates the political as it requires compliance and forgoing contestation to best fulfill its role and to produce satisfying outcomes. (shrink)

Cases of research misconduct in the ecological and environmental sciences appear to be relatively rare. A controversial paper published in Science in 2016 documenting the effects of microplastics on the feeding and innate behaviours of fish larvae has recently been retracted, with the authors found guilty of scientific misconduct. In addition to the expected fallout, such as individual and institutional reputational damage from a research misconduct finding, this case has two possibly wider-ranging ramifications. Firstly, there may be a presumptive notion (...) that a strong negative effect could be more successfully published than a neutral effect. This presumption would belie the true stringency and rigor of research adopted by workers in the field. Secondly, the case may have a negative impact on the public’s perception of and trust in legitimate and good science that addresses critical environmental issues, such as anthropogenic climate change. (shrink)

Much bioethical literature and policy guidances for big data analytics in biomedical research emphasize the importance of trust. It is essential that potential participants trust so they will allow their data to be used to further research. However, comparatively, little guidance is offered as to what trustworthy oversight mechanisms are, or how policy should support them, as data are collected, shared, and used. Generally, “trust” is not characterized well enough, or meaningfully enough, for the term to be systematically applied in (...) policy development. Yet points made in the philosophical literature on trust can help. They allow us, not only to better distinguish the different ways the term “trust” may be interpreted, but also to better determine how different approaches to trust can align with policy and governance—in what ways they may relate to key bioethical concepts and related laws, and in what ways they can help to balance individual and group interests in data sharing. This article draws from the philosophical literature on trust to identify a relationship among consent, trust, and justice. Specifically, parallels are drawn between “character-trustworthiness” and “natural justice,” a set of widely held legal safeguards intended to ensure decision-makers follow a pattern of procedural fairness which protects the rights of the individual and thereby maintains public confidence in the decision-making process. Relevance to traditional bioethical principles, established laws, and consent procedures are addressed throughout. In conclusion, policy actions are suggested. (shrink)

This paper critically examines the extent to which health promoting wearable technologies can provide people with greater autonomy over their health. These devices are frequently presented as a means of expanding the possibilities people have for making healthier decisions and living healthier lives. We accept that by collecting, monitoring, analysing and displaying biomedical data, and by helping to underpin motivation, wearable technologies can support autonomy over health. However, we argue that their contribution in this regard is limited and that—even with (...) respect to their ‘autonomy enhancing’ potential—these devices may deliver costs as well as benefits. We proceed by rehearsing the distinction that can be drawn between procedural autonomy and substantive-relational autonomy. While the information provided by wearable technologies may support deliberation and decision-making, in isolation these technologies do little to provide substantive opportunities to act and achieve better health. As a consequence, wearable technologies risk generating burdens of anxiety and stigma for their users and reproducing existing health inequalities. We then reexamine the extent to which wearable technologies actually support autonomous deliberation. We argue that wearable technologies that subject their users to biomedical and consumerist epistemologies, norms and values also risk undermining processes of genuinely autonomous deliberation. (shrink)

Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align (...) with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms. (shrink)

Zusammenfassung Problemhintergrund Biomedizinische Forschung auf Grundlage von Big Data bietet immense Vorteile. Groß angelegte multizentrische Forschung, die große Mengen persönlicher Gesundheitsdaten einbezieht, v. a. genetische und genomische Daten, könnte zu einer stärker personalisierten Medizin beitragen. Dieser Typ Forschung erfordert den Transfer und die Speicherung hochsensibler Daten, wodurch sich die Frage ergibt, wie Datensubjekte vor „data harm“ geschützt werden können, etwa vor Verletzungen der Privatsphäre, Disempowerment, Verlust von Rechten und Ausbeutung. Hier ergibt sich das Dilemma, wie die Vorteile der auf Big (...) Data basierenden biomedizinischen Forschung zu nutzen sind und zugleich das Recht von Datensubjekten auf informationelle Selbstbestimmung zu schützen ist. Argumentation Blockchain-Technologien werden aufgrund ihrer Features häufig als technische Lösung des genannten Dilemmas diskutiert. Dazu gehören die Herkunftsbestimmung von Daten, Dezentralisierung, Unveränderbarkeit sowie Zugang und das Governance-System. Allerdings wirft die Implementierung von Blockchain-Technologien in der biomedizinischen Forschung auch Fragen auf, besonders hinsichtlich Consent, rechtlicher Rahmenbedingungen und Integration in den Workflow. Somit bedarf es begleitender Maßnahmen, die ich als „enabler“ bezeichne, um das Potenzial von Blockchain-Technologien aktualisieren zu können. Zu diesen gehören innovative Consent-Modelle, Modelle zum Datenbesitzrecht und regulatorische Modelle. Schlussfolgerung Blockchain-Technologien als bloß technische Lösung sind unzureichend hinsichtlich des beschriebenen Dilemmas. Die Kombination aus dieser technischen Lösung und den genannten „enablers“ könnte der richtige Weg sein, um auf Big Data fußende biomedizinische Forschung zu ermöglichen und zugleich die informationelle Selbstbestimmung von Datensubjekten zu schützen. (shrink)

Definition of the problem The research and regulatory levels for data-intensive research in medicine are divergent. This results in a heterogeneous global field of regulating institutions with regionally unequal regulations, both in terms of the depth and restrictiveness of regulations. Despite or precisely because of the lack of globally binding regulation, nonbinding or only partially binding normative guidelines can also serve as orientation. But how should such normative regulation be designed in view of data-intensive research in medicine and what should (...) it be based on? Arguments The question of how concrete and in detail such research should be regulated can only be answered on a case-by-case basis for specific situations and institutions. Whether new norms or weightings of norms should be introduced, or whether the norms and their weightings should remain in place, can only be answered with reference to strong normative arguments. So far, there is a lack of compelling arguments in favour of new standards in view of data-intensive medical research. However, it is always necessary to translate norms, whether new or old, and apply them to a new method or technology. As in the past, both established and transparently communicated standards and an independent judgement on the application of these standards are therefore required. Conclusion In view of their particular suitability for this task, ethics committees should continue to be required and qualified as a procedural authority in the approval of data-intensive research in medicine; as an independent institution, they should make a judgement appropriate to the individual case on the basis of existing norms and standards. However, the changing practices of data-intensive research projects can also be reflected in new demands on research ethics committees, which should be addressed with reforms of their operating mode. (shrink)

This paper provides the first comprehensive analysis of ethical issues raised by artificial intelligence (AI) in veterinary medicine for companion animals. Veterinary medicine is a socially valued service, which, like human medicine, will likely be significantly affected by AI. Veterinary AI raises some unique ethical issues because of the nature of the client–patient–practitioner relationship, society’s relatively minimal valuation and protection of nonhuman animals and differences in opinion about responsibilities to animal patients and human clients. The paper examines how these distinctive (...) features influence the ethics of AI systems that might benefit clients, veterinarians and animal patients—but also harm them. It offers practical ethical guidance that should interest ethicists, veterinarians, clinic owners, veterinary bodies and regulators, clients, technology developers and AI researchers. (shrink)

Big data and artificial intelligence (“AI”) promise to transform virtually all aspects of biomedical research and health care (Matheny et al. 2019), through facilitation of drug development, diagno...

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...) would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced. (shrink)

The European Union faced high risks from personal data proliferation to individuals’ privacy. Legislation has emerged that seeks to articulate all interests at stake, balancing the need for data flow from EU countries with protecting personal data: the General Data Protection Regulation. One of the mechanisms established by this new law to strengthen the individual’s control over their data is the so-called “right to be forgotten”, the right to obtain from the controller the erasure of records. In gender transition, this (...) right represents a powerful form of control over personal data, especially health data that may reveal a gender with which they do not identify and reject. Therefore, it is pertinent to discern whether the right to have personal data deleted—in particular, health data—is ethically acceptable in gender transition. Towards addressing the ethical dimensions of the right to be forgotten in this case, this study presents relevant concepts, briefly outlines history, ethics and law of records considering the evolution from paper to electronic format, the main aspects of identity construction and gender identity, and explores the relationship between privacy, data protection/information control and identity projection. Also, it discusses in gender transition the relation between “the right to self-determination”, “the right to delete”, and “the right to identity and individuality”. Conclusions on the ethical admissibility of the ‘right to be forgotten’ to control gender-affirming information are presented. (shrink)