The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...) of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows that science to slip into (...) the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems (...) medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)

An individual's health, genetic, or environmental-exposure data, placed in an online repository, creates a valuable shared resource that can accelerate biomedical research and even open opportunities for crowd-sourcing discoveries by members of the public. But these data become “immortalized” in ways that may create lasting risk as well as benefit. Once shared on the Internet, the data are difficult or impossible to redact, and identities may be revealed by a process called data linkage, in which online data sets are matched (...) to each other. Reidentification, the process of associating an individual's name with data that were considered deidentified, poses risks such as insurance or employment discrimination, social stigma, and breach of the promises often made in informed-consent documents. At the same time, re-ID poses risks to researchers and indeed to the future of science, should re-ID end up undermining the trust and participation of potential research participants. The ethical challenges of online data sharing are heightened as so-called big data becomes an increasingly important research tool and driver of new research structures. Big data is shifting research to include large numbers of researchers and institutions as well as large numbers of participants providing diverse types of data, so the participants’ consent relationship is no longer with a person or even a research institution. In addition, consent is further transformed because big data analysis often begins with descriptive inquiry and generation of a hypothesis, and the research questions cannot be clearly defined at the outset and may be unforeseeable over the long term. In this article, we consider how expanded data sharing poses new challenges, illustrated by genomics and the transition to new models of consent. We draw on the experiences of participants in an open data platform—the Personal Genome Project—to allow study participants to contribute their voices to inform ethical consent practices and protocol reviews for big-data research. (shrink)

Scientists and clinicians are starting to translate genomic discoveries from research labs to the clinical setting. In the process, big data genomic technologies are both a risk to individual privacy and a benefit to personalized medicine. There is an opportunity to address the social and ethical demands of various stakeholders and shape the adoption of diagnostic genome technologies. We discuss ethical and practical issues associated with the networking of genomics by comparing how the European Union and North America understand and (...) practice notions of privacy and consent in research. An overview of international policy suggests the embedding of genomics within digital networks and the Internet creates conditions that challenge the management of privacy and consent in the age of big data. The risks of re-identification, informational harms, and data security vulnerabilities are issues that need to be better addressed in the clinical setting to reconcile the unpredictable pathway of research and practice in the networked information society. (shrink)

Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.

(2013). Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research. The American Journal of Bioethics: Vol. 13, No. 4, pp. 40-42. doi: 10.1080/15265161.2013.768864.

Not long ago I visited the Personal Genome Project's website. The PGP describes its mission as “creating public genome, health, and trait data.” In the “Participant Profiles” section, I found several entries that disclosed the names of individuals along with their date of birth, sex, weight, height, blood type, race, health conditions, medications, allergies, medical procedures, and more. Other profiles did not feature names but provided all of the other details. I had no special access to this information. It is (...) available to absolutely anyone with Internet access. The PGP is part of a trend known as “open data.” Many government and private entities have launched initiatives to compile very large data resources and to make them available to the public. President Obama himself has endorsed open data by issuing a May 2013 executive order directing that, to the extent permitted by law, the federal government must release its data to the public in forms that make it easy to locate, access, and use. (shrink)

The accelerating adoption of electronic health record systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In (...) addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic “big data,” in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies. (shrink)

The accelerating adoption of electronic health record systems will have profound impacts on clinical care. It will also have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs.Much has been written about the risk of EHR privacy breaches. This (...) paper focuses on a different set of concerns, those relating to data quality. Unlike clinical trial data, EHR data is not recorded primarily to meet the needs of researchers. Because of clinicians’ workloads, poor user-interface design, and other factors, EHR data is surprisingly likely to be erroneous, miscoded, fragmented, and incomplete. Although EHRs eliminate the problem of cryptic handwriting, other kinds of errors are more common with EHRs than with paper records. (shrink)

BackgroundPublic involvement activities are promoted as measures for ensuring good governance in challenging fields, such as biomedical research and innovation. Proponents of public involvement activities include individual researchers as well as non-governmental and governmental organizations. However, the concept of ‘public’ in public involvement deserves more attention by researchers because it is not purely theoretical: it has important practical functions in the guidance, evaluation and translation of public involvement activities.DiscussionThis article focuses on collective agency as one property a public as a (...) small group of participants in a public involvement activity could exhibit. It introduces a prominent theoretical approach to collective agents as one specific kind of social entities and demonstrates how this approach can be applied to current practice in public involvement activities. A brief discussion of different types of representation as they are used in the existing literature on this topic is also included because representation and collective agency can be closely related to each other. Suggestions and ideas that are derived from this reasoning include the proposal to use a ‘validity check’ for the generation of collective agents as a regular element of certain types of public involvement activities, the consequences of combining collective agency and representativeness as a further property a public could exhibit, and standards for reporting the content of public involvement activities in scientific publications.SummaryThis article discusses the importance of the concept of ‘public’ in public involvement activities, with a focus on biomedical research and innovation. It introduces various practically relevant ideas that are based on a theoretical analysis of collective agency as an important property a public can possess. (shrink)