AgeTech involves the use of emerging technologies to support the health, well-being and independent living of older adults. In this paper we focus on how AgeTech based on artificial intelligence (AI) may better support older adults to remain in their own living environment for longer, provide social connectedness, support wellbeing and mental health, and enable social participation. In order to assess and better understand the positive as well as negative outcomes of AI-based AgeTech, a critical analysis of ethical design, digital (...) equity, and policy pathways is required. A crucial question is how AI-based AgeTech may drive practical, equitable, and inclusive multilevel solutions to support healthy, active ageing. In our paper, we aim to show that a focus on equity is key for AI-based AgeTech if it is to realize its full potential. We propose that equity should not just be an extra benefit or minimum requirement, but the explicit aim of designing AI-based health tech. This means that social determinants that affect the use of or access to these technologies have to be addressed. We will explore how complexity management as a crucial element of AI-based AgeTech may potentially create and exacerbate social inequities by marginalising or ignoring social determinants. We identify bias, standardization, and access as main ethical issues in this context and subsequently, make recommendations as to how inequities that stem form AI-based AgeTech can be addressed. (shrink)

Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities (...) of digitalisation. The aim of the study was to explore expert opinions regarding current challenges and opportunities related to data-driven medicine and medical research and to provide a methodological framework for empirically grounded, continuous normative reflection. To this end, we developed a heuristic tool to map and structure empirical findings for normative analysis. Using this tool, our interview material points to a polarisation between individualistic and collectivistic orientated argumentations among experts. The study shows that a multilevel analysis is required to deal with complex normative implications of data-driven approaches in medical research and healthcare. (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)

In this article, I will argue in favor of both the ethical and epistemological utility of explanations in artificial intelligence -based medical technology. I will build on the notion of “explicability” due to Floridi, which considers both the intelligibility and accountability of AI systems to be important for truly delivering AI-powered services that strengthen autonomy, beneficence, and fairness. I maintain that explicable algorithms do, in fact, strengthen these ethical principles in medicine, e.g., in terms of direct patient–physician contact, as well (...) as on a longer-term epistemological level by facilitating scientific progress that is informed through practice. With this article, I will therefore attempt to counter arguments against demands for explicable AI in medicine that are based on a notion of “whatever heals is right.” I will elucidate my elaboration on the positive aspects of explicable AI in medicine as well as by pointing out risks of non-explicable AI. (shrink)

Globally, mortality and fertility rates generally fall as resource abundance increases. This pattern represents an evolutionary paradox insofar as resource-rich ecological contexts can support higher numbers of offspring, a component of biological fitness. This paradox has not been resolved, in part because the relationships between fertility, life history strategies, reproductive behavior, and socioeconomic conditions are complex and cultural-historically contingent. We aim to understand how we might make sense of this paradox in the specific context of late-twentieth-century, mid–demographic transition Chile. We (...) use distribution-specific generalized linear models to analyze associations between fertility-related life-history traits—number of offspring, ages at first and last reproduction, average interbirth interval, and average number of live births per reproductive span year—and socioeconomic position (SEP) using data from a cohort of 6,802 Chilean women born between 1961 and 1970. We show that Chilean women of higher SEP have shorter average interbirth intervals, more births per reproductive span year, later age at first reproduction, earlier ages at last reproduction, and, ultimately, fewer children than women of lower SEP. Chilean women of higher SEP consolidate childbearing over a relatively short time span in the middle of their reproductive careers, whereas women of lower SEP tend to reproduce over the entirety of their reproductive lifespans. These patterns may indicate that different SEP groups follow different pathways toward declining fertility during the demographic transition, reflecting different life-history trade-offs in the process. (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)

Increasing privacy concerns are arising from expanding use of aggregated personal information in health practices. Conversely, in light of the promising benefits of data driven healthcare, privacy...

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)

BackgroundThe success of biobanking is directly linked to the willingness of people to donate their biological materials for research and storage. Ethical issues related to patient consent are an essential component of the current biobanking agenda. The majority of data available are focused on population-based biobanks in USA, Canada and Western Europe. The donation decision process and its ethical applications in clinical populations and populations in countries with other cultural contexts are very limited. This study aimed to evaluate the decision-making (...) experience of the clinical biobank donors, as well as psychological and social motivators and deterrents of this decision and associated ethical risks.MethodsSemi-structured interviews were conducted in two medical institutions, in St Petersburg (Russia), in 2016–2017, among 13 donors of a clinical biobank (pregnant women, cardiac patients, and patients with multiple sclerosis) and three donation organisers—medical specialists involved in recruiting donors for a clinical biobank. Analysis of interview data was based on qualitative content analysis.ResultsDonors of a clinical biobank express beliefs in the absence of risks associated with the donation. The primary motivators for donating to the biobank were: prosocial, indirect reciprocity (response to or anticipation of an act in kind by a third party), intrinsic motivation (to enhance their self-esteem and satisfying their curiosity about the donation process), and comparability with personal values. A high level of trust in biomedical research and the particular physician can contribute to a favourable decision. The overall decision-making process regarding the biobank donation could be described as quick and not based on a careful reading of informed consent documents. The integration of biobank donation decision-making in the process of medical care might prompt patient to donate to biobank without proper consideration. The specific type of therapeutic misconception—the presence of unrealistic hope that donation could provide a direct benefit for a third person in need was discovered.ConclusionsPatients recruited to a clinical biobank in Russia have virtually no concerns as to the storage of their biomaterials. The donation decision is mainly motivated by prosocial attitudes and other factors that are similar to the motivating factors of blood donation. The fact of going through inpatient treatment and poor differentiation between donation for other people's benefit and for research purposes can make the process of obtaining consent more ethically problematic. (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...

The rapid and dynamic nature of digital transformation challenges companies that wish to develop and deploy novel digital technologies. Like other actors faced with this transformation, companies need to find robust ways to ethically guide their innovations and business decisions. Digital ethics has recently featured in a plethora of both practical corporate guidelines and compilations of high-level principles, but there remains a gap concerning the development of sound ethical guidance in specific business contexts. As a multinational science and technology company (...) faced with a broad range of digital ventures and associated ethical challenges, Merck KGaA has laid the foundations for bridging this gap by developing a Code of Digital Ethics (CoDE) tailored for this context. Following a comprehensive analysis of existing digital ethics guidelines, we used a reconstructive social research approach to identify 20 relevant principles and derive a code designed as a multi-purpose tool. Versatility was prioritised by defining non-prescriptive guidelines that are open to different perspectives and thus well-suited for operationalisation for varied business purposes. We also chose a clear nested structure that highlights the relationships between five core and fifteen subsidiary principles as well as the different levels of reference—data and algorithmic systems—to which they apply. The CoDE will serve Merck KGaA and its new Digital Ethics Advisory Panel to guide ethical reflection, evaluation and decision-making across the full spectrum of digital developments encountered and undertaken by the company whilst also offering an opportunity to increase transparency for external partners, and thus trust. (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)

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)

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)

In discussions on the ethics of genome collections and biobanks, the main worry about whether we are permitted to collect and use individuals’ genomic and genetic data is the potential for the violation of individuals’ right to informational privacy. Yet, if we do not permit these endeavors, we risk giving up on the future benefits of biomedical research. In this paper, I describe a private venture in blockchain genomics that seeks to provide an apt solution to concerns about potential privacy (...) violations in genome collections and biobanks. I then provide some reasons to doubt the kind of solution to the dilemma that such start-ups propose. I argue that the sort of autonomy that grounds the value of the right to informational privacy cannot be secured with blockchain technology alone. So, blockchain genomics ventures are insufficient to establish the permissibility of genome collections and biobanks. I then discuss an additional ethical challenge to genomic biobanks. This challenge takes the form of doubting that future benefits that result from biomedical research dependent on genomic databases will outweigh the costs. Despite criticisms of genomic research, I claim that it is reasonable to think that future net benefits will be gained from research on such databases. Therefore, I conclude that there remains a dilemma in the ethics of genomic biobanks. (shrink)

Big Data has amplified some challenges in the healthcare context. One significant challenge is how to use healthcare big data in ways that honor individual rights to informed consent or privacy. Careful analysis from diverse backgrounds will be vital in contributing ethical guidelines that can adequately address healthcare Big Data's growing complexities globally. Especially, the study argues that an under-explored African philosophy of Ubuntu can usefully influence big data practices in ways that address this challenge without undermining its benefits. Ubuntu (...) emphasizes harmonious relationships. Harmonious relations entail identifying with one another and exhibiting solidarity to each other. One can identify or exhibit solidarity with others through psychological attitudes such as thinking of oneself as part of a “we” and acting in ways that will more likely improve the quality of life of others. The African relational philosophy of Ubuntu deserves to be given an audience not only for epistemic justice but also because the continued absence of African perspective in the discourse on ethical use of HBD science represents a missed opportunity to enrich ethical thinking about HBD from diverse backgrounds. Research is, however, required to provide greater specificity on how Ubuntu values may be integrated into HBD analytic techniques. (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)

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)

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)

As information technologies have become synonymous with progress in modern society, several ethical concerns have surfaced about their societal implications. In the past few decades, information technologies have had a value-laden impact on social evolution. However, there is limited agreement on the responsibility of businesses and innovators concerning the ethical aspects of information technologies. There is a need to understand the role of business incentives and attitudes in driving technological progress and to understand how they steer the ethics discourse on (...) technology. In the information technology industry, there is an observed trivialization of ethics supported by a business driven and technology-centric approach to ethics. This trivialization rests on hardened beliefs, ideologies, and arguments which hint at reduced accountability for business and tend to individualize social responsibility. The phenomenon of ethics trivialization needs to be duly addressed to resolve the tensions between business needs and ethical concerns. This paper has identified from literature and conceptually analyzed the beliefs and ideologies underlying ethics trivialization which undermine ethics in business contexts. The paper has attempted to address the business concerns indicated by this phenomenon as well as highlight the weaknesses of its assumptions, rhetoric, and justifications. The aim of this paper is to systematically present the justifications in favor of and against the practice of ethics trivialization in the information technology domain, thereby highlighting the need to develop frameworks for assessment of ethical responsibility, accountability, and democratization of the value trade-offs involved in the design of technologies. (shrink)

BackgroundConsent for data research in acute and critical care is complex as patients become at least temporarily incapacitated or die. Existing guidelines and regulations in the European Union are of limited help and there is a lack of literature about the use of data from this vulnerable group. To aid the creation of a patient-centred framework for responsible data research in the acute setting, we explored views of patients and next-of-kin about the collection, storage, sharing and use of genetic and (...) health-related data for observational research.MethodsWe conducted qualitative interviews (n = 19) with Dutch sudden cardiac arrest survivors who donated clinical and socio-economic data and genetic samples to research. We also interviewed their next-of-kin. Topics were informed by ethics literature and we used scenario-sketches to aid discussion of complex issues.ResultsSudden cardiac arrest survivors displayed limited awareness of their involvement in health data research and of the content of their given consent. We found that preferences regarding disclosure of clinically actionable genetic findings could change over time. When data collection and use were limited to the medical realm, patients trusted researchers to handle data responsibly without concern for privacy or other risks. There was no consensus as to whether deferred consent should be explicitly asked from survivors. If consent is asked, this would ideally be done a few months after the event when cognitive capacities have been regained. Views were divided about the need to obtain proxy consent for research with deceased patients’ data. However, there was general support for the disclosure of potentially relevant post-mortem genetic findings to relatives.ConclusionsSudden cardiac arrest patients’ donation of data for research was grounded in trust in medicine overall, blurring the boundary between research and care. Our findings also highlight questions about the acceptability of a one-time consent and about responsibilities of patients, researchers and ethics committees. Finally, further normative investigation is needed regarding the (continued) use of participants’ data after death, which is of particular importance in this setting. Our findings are thought to be of relevance for other acute and life-threatening illnesses as well. (shrink)

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media. These AI depression detectors (AIDDs) identify users who are at risk of depression prior to any contact with the healthcare system. The article focuses on the ethical implications of AIDDs regarding affected users’ health-related autonomy. Firstly, it presents the (ethical) discussion of AI (...) in medicine and, specifically, in mental health. Secondly, two models of AIDDs using social media data and different usage scenarios are introduced. Thirdly, the concept of patient autonomy, according to Beauchamp and Childress, is critically discussed. Since this concept does not encompass the specific challenges linked with the digital context of AIDDs in social media sufficiently, the current analysis suggests, finally, an extended concept of health-related digital autonomy. (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)

Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors and embeds the analysis in (...) ethical debates on data sharing and biobank-related data governance. We observe efforts to perform a paradigmatic shift of the discourse around the General Data Protection Regulation-implementation away from ‘protecting data’ as key concern to ‘protecting health’ of individuals and societies at large. Instead of data protection, the key risks stressed by health researchers became potential obstacles to research. In line, exchange of information with data subjects is not a key concern in the arguments of biobank-related actors and it is assumed that patients want ‘their’ data to be used. We interpret these narratives as a ‘reaction’ to potential restrictions for data reuse and in line with a broader trend towards Big Data science, as the very idea of biobanking is conceptualized around long-term use of readily prepared data. We conclude that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society. (shrink)

In recent years, the healthcare field welcomed an emerging field of practices captured under the umbrella term ‘Big Data’. This term is surrounded with positive rhetoric and promises about the ability to analyse real-world data quickly and comprehensively. Such rhetoric is highly consequential in shaping debates on Big Data. While the fields of Science and Technology Studies and Critical Data Studies have been instrumental in elaborating the neglected and problematic dimensions of Big Data, it remains an open question how and (...) to what extent such insights become embedded in other fields. In this paper, we analyse the epistemological claims that accompany Big Data in the healthcare domain. We systematically searched scientific literature and selected 206 editorials as these reflect on developments in the domain. Through an interpretive analysis, we construct five ideal-typical discourses that all frame Big Data in specific ways. Three of the discourses frame Big Data in positive terms and disseminate a compelling rhetoric. Metaphors of ‘capturing’, ‘illuminating’ and ‘harnessing’ data presume that Big Data are benign and leading to valid knowledge. The scientist and critical-interpretive discourses question the objectivity and effectivity claims of Big Data. Metaphors of ‘selecting’ and ‘constructing’ data illustrate another political message, framing Big Data as limited. We conclude that work in the critical-interpretive discourse has not broadly infiltrated the medical domain. Ways to better integrate aspects of the discourse in the healthcare domain are urgently needed. (shrink)

There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ counter-mapping to map government big data practices and internal discussions (...) of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention. (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)

Enacting an AI system typically requires three iterative phases where AI engineers are in command: selection and preparation of the data, selection and configuration of algorithmic tools, and fine-tuning of the different parameters on the basis of intermediate results. Our main hypothesis is that these phases involve practices with ethical questions. This paper maps these ethical questions and proposes a way to address them in light of a neo-republican understanding of freedom, defined as absence of domination. We thereby identify different (...) types of responsibility held by AI engineers and link them to concrete suggestions on how to improve professional practices. This paper contributes to the literature on AI and ethics by focusing on the work necessary to configure AI systems, thereby offering an input to better practices and an input for societal debates. (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)

As a relational concept, responsible innovation can be made more tangible by asking innovation of what and responsibility of whom for what? Arranging the scattered field of responsible innovation comprehensively, starting from an anthropological point of view, into five fields of tension and five categories of spearheads, may be theoretically and practically helpful while offering suggestions for both research and management.

BackgroundLarge-scale, centralized data repositories are playing a critical and unprecedented role in fostering innovative health research, leading to new opportunities as well as dilemmas for the medical sciences. Uncovering the reasons as to why citizens do or do not contribute to such repositories, for example, to population-based biobanks, is therefore crucial. We investigated and compared the views of existing participants and non-participants on contributing to large-scale, centralized health research data repositories with those of ex-participants regarding the decision to end their (...) participation. This comparison could yield new insights into motives of participation and non-participation, in particular the behavioural change of withdrawal.MethodsWe conducted 36 in-depth interviews with ex-participants, participants, and non-participants of a three-generation, population-based biobank in the Netherlands. The interviews focused on the respondents’ decision-making processes relating to their participation in a large-scale, centralized repository for health research data.ResultsThe decision of participants and non-participants to contribute to the biobank was motivated by a desire to help others. Whereas participants perceived only benefits relating to their participation and were unconcerned about potential risks, non-participants and ex-participants raised concerns about the threat of large-scale, centralized public data repositories and public institutes, such as social exclusion or commercialization. Our analysis of ex-participants’ perceptions suggests that intrapersonal characteristics, such as levels of trust in society, participation conceived as a social norm, and basic societal values account for differences between participants and non-participants.ConclusionsOur findings indicate the fluidity of motives centring on helping others in decisions to participate in large-scale, centralized health research data repositories. Efforts to improve participation should focus on enhancing the trustworthiness of such data repositories and developing layered strategies for communication with participants and with the public. Accordingly, personalized approaches for recruiting participants and transmitting information along with appropriate regulatory frameworks are required, which have important implications for current data management and informed consent procedures. (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.

The concept of ‘ownership’ is increasingly central to debates, in the media, health policy and bioethics, about the appropriate management of clinical data. I argue that the language of ownership acts as a metaphor and reflects multiple concerns about current data use and the disenfranchisement of citizens and collectives in the existing data ecosystem. But exactly which core interests and concerns ownership claims allude to remains opaque. Too often, we jump straight from ‘ownership’ to ‘private property’ and conclude ‘the data (...) belongs to the patient’. I will argue here that private property is only one type of relevant relationship between people, communities and data. There are several reasons to doubt that conceptualising data as private property presents a compelling response to concerns about clinical data ownership. In particular I argue that clinical data are co-constructed, so a property account would fail to confer exclusive rights to the patient. A non-property account of ownership acknowledges that the data are ‘about the patient’, and therefore the patient has relevant interests, without jumping to the conclusion that the data ‘belongs to the patient’. On this broader account of ownership, the relevant harm is the severing of the connection between the patient and their data, and the solution is to re-engage and re-connect patients to the data research enterprise. (shrink)

Biomedical research using data from participants’ mobile devices borrows heavily from the ethos of the “citizen science” movement, by delegating data collection and transmission to its volunteer subjects. This engagement gives volunteers the opportunity to feel like partners in the research and retain a reassuring sense of control over their participation. These virtues, in turn, give both grass-roots citizen science initiatives and institutionally sponsored mHealth studies appealing features to flag in recruiting participants from the public. But while grass-roots citizen science (...) projects are often community-based, mHealth research ultimately depends on the individuals who own and use mobile devices. This inflects the ethos of mHealth research towards a celebration of individual autonomy and empowerment, at the expense of its implications for the communities or groups to which its individual participants belong. But the prospects of group harms — and benefits — from mHealth research are as vivid as they are in other forms of data-intensive “precision health” research, and will be important to consider in the design of any studies using this approach. (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)

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)

In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to (1) the physician’s role in the patients’ formation of and acting on personal preferences and values, (2) the bias and opacity problem of AI systems, and (3) rational concerns about the future societal effects of introducing AI systems in the health care sector.

Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data–based electronic surveillance, by commercial bodies and government agencies, which draws on a plethora of internet- and mobile device–based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can be (...) analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease events—including exotic or emerging infections—even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory records and the potential to re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control. (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)

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)