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)

Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory (...) role in providing useful generalizations to complement the procedure of prioritization. Overall this paper aims to shed light on the structure of contemporary research in molecular biology. (shrink)

We claim that in contemporary studies in molecular biology and biomedicine, the nature of ‘manipulation’ and ‘intervention’ has changed. Traditionally, molecular biology and molecular studies in medicine are considered experimental sciences, whereas experiments take the form of material manipulation and intervention. On the contrary “big science” projects in biology focus on the practice of data mining of biological databases. We argue that the practice of data mining is a form of intervention although it does not (...) require material manipulation. We also suggest that material manipulation, although still present in in the practice of data mining, fulfill a different epistemic role. (shrink)

Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed (...) to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision-making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey’s five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of manipulative (...) power to initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery). It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness-to-pay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry. (shrink)

The collection and dissemination of data on human and nonhuman organisms has become a central feature of 21st-century biology and has been endorsed by funding agencies in the United States and Europe as crucial to translating biological research into therapeutic and agricultural innovation. Large molecular data sets, often referred to as “big data,” are increasingly incorporated into digital databases, many of which are freely accessible online. These data have come to be seen as resources that (...) play a key role in mediating global market exchange, thus achieving a prominent social and economic status well beyond science itself. At the same time, calls to make all such data publicly and freely available have garnered strength and visibility, most prominently in the form of the Open Data movement. I discuss these developments by considering the conditions under which data journey across the communities and institutions implicated in globalized biology and biomedicine, and what this indicates about how Internet-based communication and the use of online databases affect scientific research and its role within contemporary society. (shrink)

This paper argues that analyses of the ways in which Big Data has been enacted in other academic disciplines can provide us with concepts that will help understand the application of Big Data to social questions. We use examples drawn from our Science and Technology Studies analyses of -omic biology and high energy physics to demonstrate the utility of three theoretical concepts: primary and secondary inscriptions, crafted and found data, and the locus of legitimate interpretation. These (...) help us to show how the histories, organisational forms, and power dynamics of a field lead to different enactments of big data. The paper suggests that these concepts can be used to help us to understand the ways in which Big Data is being enacted in the domain of the social sciences, and to outline in general terms the ways in which this enactment might be different to that which we have observed in the ‘hard’ sciences. We contend that the locus of legitimate interpretation of Big Data biology and physics is tightly delineated, found within the disciplinary institutions and cultures of these disciplines. We suggest that when using Big Data to make knowledge claims about ‘the social’ the locus of legitimate interpretation is more diffuse, with knowledge claims that are treated as being credible made from other disciplines, or even by those outside academia entirely. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

We live in the Age of Big Data. In medicine, artificial intelligence and machine learning algorithms, fueled by big data, promise to change how physicians make diagnoses, determine prognoses, and develop new treatments. An exponential rise in articles on these topics is seen in the medical literature. Recent applications range from the use of deep learning neural networks to diagnose diabetic retinopathy and skin cancer from image databases, to the use of various machine learning algorithms for prognostication in (...) cancer and cardiovascular disease. Many factors are driving the adoption of AI in health care, from... (shrink)

Defense of Scientific Hypothesis: From Reproducibility Crisis to Big Data sets out to explain and defend the scientific hypothesis. Alger's mission is to counteract the misinformation and misunderstanding about the hypothesis that even seasoned scientists have concerning its nature and place in modern science. Most biological scientists receive little or no formal training in scientific thinking. Further, the hypothesis is under attack by critics who claim that it is irrelevant to science. In order to appreciate and evaluate scientific controversies (...) like global climate change, vaccine safety, etc., the public first needs to understand the hypothesis. Defense of Scientific Hypothesis begins by describing and analyzing the scientific hypothesis in depth and examining its relationships to various kinds of science. Alger then guides readers through a review of the hypothesis in the context of the Reproducibility Crisis and presents survey data on how scientists perceive and employ hypotheses. He assesses cognitive factors that influence our ability to use the hypothesis and makes practical and policy recommendations for teaching and learning about it. Finally, Alger considers two possible futures of the hypothesis in science as the Big Data revolution looms: in one scenario, the hypothesis is displaced by the Big Data Mindset that forgoes understanding in favor of correlation and prediction. In the other, robotic science incorporates the hypotheses into mechanized laboratories guided by artificial intelligence. But in his illuminating epilogue, Alger envisions a third way, the Centaur Scientist, a symbiotic relationship between human scientists and computers. (shrink)

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are (...) pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services. (shrink)

As with many other countries, Malaysia is also developing and promoting biomedical research to increase the understanding of human diseases and possible interventions. To facilitate this development, there is a significant growth of biobanks in the country to ensure continuous collection of biological samples for future research, which contain extremely important personal information and health data of the participants involved. Given the vast amount of samples and data accumulated by biobanks, they can be considered as reservoirs of precious (...) biomedical big data. It is therefore imperative for biobanks to have in place regulatory measures to ensure ethical use of the biomedical big data. Malaysia has yet to introduce specific legislation for the field of biobanking. However, it can be argued that its existing Personal Data Protection Act 2010 has laid down legal principles that can be enforced to protect biomedical big data generated by the biobanks. Consent is a mechanism to enable data subjects to exercise their autonomy by determining how their data can be used and ensure compliance with legal principles. However, there are two main concerns surrounding the current practice of consent in biomedical big data in Malaysia. First, it is uncertain that the current practice would be able to respect the underlying notion of autonomy, and second, it is not in accordance with the legal principles of the PDPA. Scholars have deliberated on different strategies of informed consent, and a more interactive approach has recently been introduced: dynamic consent. It is argued that a dynamic consent approach would be able to address these concerns. (shrink)

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in (...) biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. (shrink)

[Without a theorising], a man might as well go into a gravel-pit and count the pebbles and describe the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service!Recent years have seen a steady shift of funding programs in biomedical research towards data collection, analysis, and management and its computational analysis. A sizable majority of new major funding opportunity announcements call for (...) assembling large sample banks and databases of molecular profiles of diseases or for computational tools for analysis, display, and distribution of the data. Large consortium projects, such as the Cancer Genome Atlas, the UK Biobank (Allen... (shrink)

This article provides an ethnographic account of how Big Data biology is produced, interpreted, debated, and translated in a Big Data-driven cancer clinical trial, entitled “Personalized OncoGenomics,” in Vancouver, Canada. We delve into epistemological differences between clinical judgment, pathological assessment, and bioinformatic analysis of cancer. To unpack these epistemological differences, we analyze a set of gazes required to produce Big Data biology in cancer care: clinical gaze, molecular gaze, and informational gaze. We are concerned with (...) the interactions of these bodily gazes and their interdependence on each other to produce Big Data biology and translate it into clinical knowledge. To that end, our central research questions ask: How do medical practitioners and data scientists interact, contest, and collaborate to produce and translate Big Data into clinical knowledge? What counts as actionable and reliable data in cancer decision-making? How does the explicability or translatability of genomic Big Data come to redefine or contradict medical practice? The article contributes to current debates on whether Big Data engenders new questions and approaches to biology, or Big Data biology is merely an extension of early modern natural history and biology. This ethnographic account will highlight how genomic Big Data, which underpins the mechanism of personalized medicine, allows oncologists to understand and diagnose cancer in a different light, but it does not revolutionize or disrupt medical oncology on an institutional level. Rather, personalized medicine is interdependent on different styles of thought, gaze, and practice to be produced and made intelligible. (shrink)

Dominant forms of contemporary big-data based digital citizen science do not question the institutional divide between qualified experts and lay-persons. In our paper, we turn to the historical case of a large-scale amateur project on biogeographical birdwatching in the late nineteenth and early twentieth century to show that networked amateur research can operate in a more autonomous mode. This mode depends on certain cultural values, the constitution of specific knowledge objects, and the design of self-governed infrastructures. We conclude by (...) arguing that the contemporary quest for autonomous citizen science is part of a broader discourse on the autonomy of scientific research in general. Just as the actors in our historical case positioned themselves against the elitism of gentlemen scientists, avant-garde groups of the twenty first century like biohackers and civic tech enthusiasts position themselves against the system of professional science—while “digital citizen science” remains to oscillate between claims for autonomy and realities of heteronomy, constantly reaffirming the classic lay-expert divide. (shrink)

There has been a spurt in both fundamental and translational research that examines the underlying mechanisms of the human microbiome in psychiatric disorders. The personalized and dynamic features of the human microbiome suggest the potential of its manipulation for precision psychiatry in ways to improve mental health and avoid disease. However, findings in the field of microbiome also raise philosophical and ethical questions. From a philosophical point of view, they may yet be another attempt at providing a biological cause for (...) phenomena that ultimately cannot be so easily localized. From an ethical point of view, it is relevant that the human gut microbiome comprises data on the individual’s lifestyle, disease history, previous medications, and mental health. Massive datasets of microbiome sequences are collected to facilitate comparative studies to identify specific links between the microbiome and mental health. Although this emerging research domain may show promise for psychiatric patients, it is surrounded by ethical challenges regarding patient privacy, health risks, effects on personal identity, and concerns about responsibility. This narrative overview displays the roles and advances of microbiome research in psychiatry and discusses the philosophical and ethical implications of microbiome big data and microbiome-based interventions for psychiatric patients. We also investigate whether these issues are really “new,” or “old wine in new bottles.”. (shrink)

A central theme in western philosophy was to find formal methods that can reliably discover empirical relationships and their explanations from data assembled from experience. As a philosophical project, that ambition was abandoned in the 20th century and generally dismissed as impossible. It was replaced in philosophy by neo-Kantian efforts at reconstruction and justification, and in professional statistics by the more limited ambition to estimate a small number of parameters in pre-specified hypotheses. The influx of “big data” from (...) climate science, neuropsychology, biology, astronomy and elsewhere implicitly called for a revival of the grander philosophical ambition. Search algorithms are meeting that call, but they pose a problem: how are their accuracies to be assessed in domains where experimentation is limited or impossible? Increasingly, the answer is through simulation of data from models of the kind of process in the domain. In some cases, these innovations require rethinking how the accuracy and informativeness of inference methods can be assessed. Focusing on causal inference, we give an example from neuroscience, but to show that the model/simulation strategy is not confined to causal inference, we also consider two classification problems from astrophysics: identifying exoplanets and identifying dark matter concentrations. (shrink)

In biology—as in other scientific fields—there is a lively opposition between big and small science projects. In this commentary, I try to contextualize this opposition in the field of biomedicine, and I argue that, at least in this context, big science projects should come first.

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) theory (...) of disease, where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual to measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine to inform their use in patient stratification in personalized medicine. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest that (...) the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

Biology is seen not merely as a privileged oppressor of women but as a co-victim of masculinist social assumptions. We see feminist critique as one of the normative controls that any scientist must perform whenever analyzing data, and we seek to demonstrate what has happened when this control has not been utilized. Narratives of fertilization and sex determination traditionally have been modeled on the cultural patterns of male/female interaction, leading to gender associations being placed on cells and their (...) components. We also find that when gender biases are controlled, new perceptions of these intracellular and extracellular relationships emerge. (shrink)

The emergence of systems biology raises many fascinating questions: What does it mean to take a systems approach to problems in biology? To what extent is the use of mathematical and computational modelling changing the life sciences? How does the availability of big data influence research practices? What are the major challenges for biomedical research in the years to come? This book addresses such questions of relevance not only to philosophers and biologists but also to readers interested (...) in the broader implications of systems biology for science and society. The book features reflections and original work by experts from across the disciplines including systems biologists, philosophers, and interdisciplinary scholars investigating the social and educational aspects of systems biology. In response to the same set of questions, the experts develop and defend their personal perspectives on the distinctive character of systems biology and the challenges that lie ahead. Readers are invited to engage with different views on the questions addressed, and may explore numerous themes relating to the philosophy of systems biology. This edited work will appeal to scholars and all levels, from undergraduates to researchers, and to those interested in a variety of scholarly approaches such as systems biology, mathematical and computational modelling, cell and molecular biology, genomics, systems theory, and of course, philosophy of biology. (shrink)

Big dreams are rare but highly memorable dream experiences that make a strong and lasting impact on the dreamer's waking awareness. Moving far beyond "I forgot to study and the finals are today" and other common scenarios, such dreams can include vivid imagery, intense emotions, fantastic characters, and an uncanny sense of being connected to forces beyond one's ordinary dreaming mind. In Big Dreams, Kelly Bulkeley provides the first full-scale cognitive scientific analysis of such dreams, putting forth an original theory (...) about their formation, function, and meaning.Big dreams have played significant roles in religious and cultural history, but because of their infrequent occurrence and fantastical features, they have rarely been studied in light of modern science. We know a great deal about the religious manifestations of big dreams throughout history and around the world, but until now that cross-cultural knowledge has never been integrated with scientific research on their psychological roots in the brain-mind system. In Big Dreams, Bulkeley puts a classic psychological thesis to the scientific test by clarifying and improving it with better data, sharper analysis, and a broader evolutionary framework. He brings evidence from multiple sources, shows patterns of similarity and difference, questions prior assumptions, and provides predictive models that can be applied to new sets of data. The notion of a connection between dreaming and religion has always been intuitively compelling; Big Dreams transforms it into a solid premise of religious studies and brain-mind science. Combining evidence from religious studies, psychology, anthropology, evolutionary biology, and neuroscience, Big Dreams makes a compelling argument that big dreams are a primal wellspring of religious experience. They represent an innate, neurologically hard-wired capacity of our species that regularly provokes greater self-awareness, creativity, and insight into the existential challenges and spiritual potentials of human life. (shrink)

Both clinical research and basic science rely on the epistemic practice of extrapolation from surrogate models, to the point that explanatory accounts presented in review papers and biology textbooks are in fact composite pictures reconstituted from data gathered in a variety of distinct experimental setups. This raises two new challenges to previously proposed mechanistic-similarity solutions to the problem of extrapolation: one pertaining to the absence of mechanistic knowledge in the early stages of research and the second to the (...) large number of extrapolations underpinning explanatory accounts. An analysis of the strategies deployed in experimental research supports the conclusion that while results from validated surrogate models are treated as a legitimate line of evidence supporting claims about target systems, the overall structure of research projects also demonstrates that extrapolative inferences are not considered definitive or sufficient evidence, but only partially justified hypotheses subjected to further testing. 1 Introduction2 Surrogate Models2.1 What exactly is a surrogate model?2.2 Why use surrogate models?3 Prior Validation of Surrogate Models3.1 The validation and ranking of surrogate models in the early stages of basic research3.2 The validation of surrogate models in later stages of basic research and in clinical research4 ‘Big Picture’ Accounts and the Extrapolations Underpinning Them4.1 The mosaic nature of mechanistic descriptions in basic science4.2 Challenges for mechanistic-similarity-based validation protocols5 Retrospective Testing of Extrapolated Knowledge5.1 Holistic confirmation5.2 Fallback strategies6 Conclusions. (shrink)

This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, (...) nor can they be seen as straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and prospective usefulness as evidence. (shrink)

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that (...) data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains. (shrink)

This article argues that the fact that an action will compound a prior injustice counts as a reason against doing the action. I call this reason The Anti-Compounding Injustice principle or aci. Compounding injustice and the aci principle are likely to be relevant when analyzing the moral issues raised by “big data” and its combination with the computational power of machine learning and artificial intelligence. Past injustice can infect the data used in algorithmic decisions in two distinct ways. (...) Sometimes prior injustice undermines the accuracy of the data itself. In these contexts, improving accuracy will also help to avoid compounding injustice. Other times, past injustice produces real-world differences among people with regard to skills, health, wealth, and other traits that employers, lenders, and others seek to measure. When decisions are based on accurate data that itself results from prior injustice, these decisions can also compound injustice. This second dynamic has received less attention than the first but is especially important because improving the accuracy of data will not mitigate this unfairness. (shrink)

There is no doubt science is currently suffering from a credibility crisis. This thought-provoking book argues that, ironically, science's credibility is being undermined by tools created by scientists themselves. Scientific disinformation and damaging conspiracy theories are rife because of the internet that science created, the scientific demand for empirical evidence and statistical significance leads to data torturing and confirmation bias, and data mining is fuelled by the technological advances in Big Data and the development of ever-increasingly powerfulcomputers. (...) Using a wide range of entertaining examples, this fascinating book examines the impacts of society's growing distrust of science, and ultimately provides constructive suggestions for restoring the credibility of the scientific community. (shrink)

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of (...) this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Matrices that cannot be handled using conventional clustering, regression or classification methods are often found in every big data research area. In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a (...) submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method’s performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods. (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)

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)

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

ABSTRACT:Technological advances introduce the possibility that, in the future, firms will be able to use big-data analysis to discover and offer consumers their individual reservation price. This can generate some interesting benefits, such as a better state of affairs in terms of equality of both welfare and resources, as well as increased social welfare. However, these benefits are countered by considerations of relational equality. This article takes up the market-failures approach as its basis to demonstrate what is wrong with (...) using big data to personalize prices. The article offers an improvement to the market-failures approach and argues that what is wrong with using big data to personalize prices is that it unfairly undermines consumers’ ability to benefit from the market, which is the very point of having a market. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make (...) sense of culture, history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either (...) limitedly or not at all, and their prospects of doing so in the future are limited too. (shrink)

ABSTRACT Scientific advances in genetic analysis have been made possible in recent years by technical developments in computational biology, or bioinformatics. Bioinformatics has opened up the human genome to diverse analyses involving automated laboratory hardware and machine learning algorithms and software. As part of an emerging field of social genomics, recent educational genetics studies using big data have begun to raise challenging findings linking DNA to predicted life outcomes. Bioinformatic technologies and techniques including ‘genome-wide association’ and ‘polygenic scoring’ (...) are producing new kinds of genetic biodata and expert knowledge for rethinking the upbringing and education of children. This article takes as an empirical opening a recent epistemic and ethical controversy over the use of biodata to make genetic predictions about educational, socio-economic and life outcomes, detailing the forms of expert knowledge and technologies involved these predictions, and exploring the social and ethical implications of data-intensive bioinformatics. (shrink)

This short article argues that an adequate response to the implications for governance raised by ‘Big Data’ requires much more attention to agency and reflexivity than theories of ‘algorithmic power’ have so far allowed. It develops this through two contrasting examples: the sociological study of social actors used of analytics to meet their own social ends and the study of actors’ attempts to build an economy of information more open to civic intervention than the existing one. The article concludes (...) with a consideration of the broader norms that might contextualise these empirical studies, and proposes that they can be understood in terms of the notion of voice, although the practical implementation of voice as a norm means that voice must sometimes be considered via the notion of transparency. (shrink)

Italian mycologist Pier Andrea Saccardo is best remembered for his monumental Sylloge Fungorum, the first ‘modern’ effort to compile all identified fungi within a single classification scheme. The existing history of mycology is limited and has primarily focused on developments within England, but this article argues that Saccardo and his collaborators on the Sylloge supported a vital transnational expansion of mycological knowledge exchange and played a crucial role in stabilizing the tangled knot of local naming and identification among the world's (...) amateur and professional mycologists. Written in the ‘universal’ scientific language of Latin, the Sylloge served as an early database of fungal knowledge and symbolized a broader unification of mycological inquiry in a moment of expanded scientific correspondence. The article situates this proto-database in broader histories of big data in biology and shows how the Sylloge formed a globalizing foundation for the twentieth century's major collecting and taxonomic advances in mycology. (shrink)

Purpose The purpose of this paper is to explore the epistemological tensions embedded within big data and data-driven technologies to advance a socio-political reconsideration of the public dimension in the assessment of their implementation. Design/methodology/approach This paper builds upon (and revisits) the European Union’s (EU) normative understanding of artificial intelligence (AI) and data-driven technologies, blending reflections rooted in philosophy of technology with issues of democratic participation in tech-related matters. Findings This paper proposes the conceptual design of sectorial (...) and/or local-level e-participation platforms to ignite an ongoing discussion – involving experts, private actors, as well as cognizant citizens – over the implementation of data-driven technologies, to avoid siloed, tech-solutionist decisions. Originality/value This paper inscribes the EU’s normative approach to AI and data-driven technologies, as well as critical work on the governance of these technologies, into a broader political dimension, suggesting a way to democratically and epistocratically opening up the decisional processes over the development and implementation of these technologies and turn such processes into a systemic civic involvement. (shrink)