The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can contribute to (...) experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of (...) data science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in (...) carrying out their work, but also the social and “human” sciences. One set of tools in particular is rapidly being adopted—the family of information technologies that fall under the umbrella of “big data.” As in the natural sciences, big data is giving researchers in the human sciences access to phenomena which they would otherwise be unable to experience and investigate. And like the former, the latter thereby shape the ways those scientists perceive and understand who and what we are. Looking at two case studies of big data-driven research in the human sciences, I begin in this paper to suggest how we might understand these phenomenological and hermeneutic changes. (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)

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)

The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. (...) Such annotation enables several objectives, including searching, browsing and cross-database data integration. A key benefit conferred by realismbased ontology is that suitably annotated research results are able to be aggregated and compared in a fashion that is based on the underlying reality that the science is studying. This has the potential of increasing the power of statistical analysis and meta-analysis in data-driven science. This aspect has been fruitfully exploited in the investigation of the functions of genes in molecular biology. Cognitive neuroscience uses functional neuroimaging to investigate the brain correlates of areas of mental functioning such as memory, planning and emotion. The use of functional neuroimaging to study affective phenomena such as the emotions is called ‘affective neuroscience’. BrainMap is the largest curated database of coordinates and metadata for studies in cognitive neuroscience, including affective neuroscience (Laird et al., 2005). BrainMap data is already classified and indexed using a terminology for classification, called the ‘Cognitive Paradigm Ontology’ (CogPO), that has been developed to facilitate searching and browsing. However, CogPO has been developed specifically for the BrainMap database, and the data are thus far not annotated to a realism-based ontology which would allow the discovery of interrelationships between research results across different databases on the basis of what the research is about. In this contribution, we describe ongoing work that aims to annotate affective neuroscience data, starting with the BrainMap database, using the Emotion Ontology. We describe our objectives and technical approach to the annotation, and mention some of the challenges. (shrink)

How a senior researcher from a developing country can build an organic academic enterprise. Drawing from childhood experience with nature, past works with the business sector, and philosophy of data-driven research, the essay presents a compelling case of letting young graduates work on big database-building projects: one on Vietnamese social sciences; the other is more than 80 years of the pioneer science in Vietnam—mathematics. Two national databases have enabled meaningful data-driven interactions with scientific policymakers.

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)

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key (...) research topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches. (shrink)

This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated (...) in the context of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy-makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, (...) and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

It is often said that ‘every logical truth is obvious’ (Quine 1970: 82), that the ‘axioms and rules of logic are true in an obvious way’ (Murawski 2014: 87), or that ‘logic is a theory of the obvious’ (Sher 1999: 207). In this chapter, I set out to test empirically how the idea that logic is obvious is reflected in the scholarly work of logicians and philosophers of logic. My approach is data-driven. That is to say, I propose (...) that systematically searching for patterns of usage in databases of scholarly works, such as JSTOR, can provide new insights into the ways in which the idea that logic is obvious is reflected in logical and philosophical practice, i.e., in the arguments that logicians and philosophers of logic actually make in their published work. (shrink)

The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...) enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins. (shrink)

This essay is a response to Luis M. Augusto’s intriguing paper on the rift between mainstream and formal ontology. I will show that there are in fact two questions at issue here: 1. concerning the links between mainstream and formal approaches within philosophy, and 2. concerning the application of philosophy (and especially philosophical ontology) in support of information-driven research for example in the life sciences.

This entry summarizes an emerging subdiscipline of both empirical bioethics and experimental philosophy (“x-phi”) which has variously been referred to as experimental philosophical bioethics, experimental bioethics, or simply “bioxphi”. Like empirical bioethics, bioxphi uses data-driven research methods to capture what various stakeholders think (feel, judge, etc.) about moral issues of relevance to bioethics. However, like its other parent discipline of x-phi, bioxphi tends to favor experiment-based designs drawn from the cognitive sciences – including psychology, neuroscience, and behavioral economics (...) – to tease out why and how stakeholders think as they do. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

I’m often asked why, as a philosopher of science, I study archaeology. Philosophy is so abstract and intellectual, and archaeology is such an earth-bound, data-driven enterprise, what could the connection possibly be? This puzzlement takes a number of different forms. In one memorable exchange in the late 1970s when I was visiting Oxford as a graduate student an elderly don, having inquired politely about my research interests, tartly observed that archaeology isn’t a science, so I couldn’t (...) possibly be writing a dissertation in philosophy of science on archaeology. At job interviews a few years later a standard opening gambit was to ask: “so what’s philosophical about the work you do on archaeology” or, more querulously, “what could possibly be of philosophical interest in archaeology”? And from archaeologists, weary of the “theory wars” that raged through 1980s and 1990s, the standard question was, “What’s the point?! Better to step away from the wrangling about philosophical ‘isms’ and get on with the empirical work that needs doing.”. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...) on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

A growing awareness of the lack of reproducibility has undermined society’s trust and esteem in social sciences. In some cases, well-known results have been fabricated or the underlying data have turned out to have weak technical foundations.

Ontologies are being developed throughout the biomedical sciences to address standardization, integration, classification and reasoning needs against the background of an increasingly data-driven research paradigm. In particular, ontologies facilitate the translation of basic research into benefits for the patient by making research results more discoverable and by facilitating knowledge transfer across disciplinary boundaries. Addressing and adequately treating mental illness is one of our most pressing public health challenges. Primary research across multiple disciplines such as psychology, psychiatry, biology, neuroscience (...) and pharmacology needs to be integrated in order to promote a more comprehensive understanding of underlying processes and mechanisms, and this need for integration only becomes more pressing with our increase in understanding of differences among individuals and populations at the molecular level concerning susceptibility to specific illnesses. Substance addiction is a particularly relevant public health challenge in the developed world, affecting a substantial percentage of the population, often co-morbid with other illnesses such as mood disorders. Currently, however, there is no straightforward automated method to combine data of relevance to the study of substance addiction across multiple disciplines and populations. In this contribution, we describe a framework of interlinked, interoperable bio-ontologies for the annotation of primary research data relating to substance addiction, and discuss how this framework enables easy integration of results across disciplinary boundaries. We describe entities and relationships relevant for the description of addiction within the Mental Functioning Ontology, Chemical Entities of Biological Interest Ontology, Protein Ontology, Gene Ontology and the Neuroscience Information Framework ontologies. (shrink)

Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for (...) which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective. (shrink)

After introducing the concept of compressed sensing as a complementary measurement mode to the classical Shannon-Nyquist approach, I discuss some of the drivers, potential challenges and obstacles to its implementation. I end with a speculative attempt to embed compressed sensing as an enabling methodology within the emergence of data-driven discovery. As a consequence I predict the growth of non-nomological sciences where heuristic correlations will find applications but often bypass conventional pure basic and use-inspired basic research stages due to (...) the lack of verifiable hypotheses. (shrink)

In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of (...) Data Science provided in the scholarly literature includes “the study of the generalizable extraction of knowledge from data” (Dhar 2013, 64), “data stewardship and data sharing…access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research” (Hartter et al., 2013, 1), and “personal information, health status, daily activities and shopping preferences that are recorded and used to give us instant feedback and recommendations based on previous online behavior.” (Shin 2013) United States universities have begun to offer graduate programs in “data science”, anticipating the growth of this field for marketing, national security, and health industries. These universities include New York University, Columbia University, Stanford, Northwestern, and Syracuse. (shrink)

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It (...) is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute …. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. (...) Using healthcare as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. (...) First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on ‘description’, ‘classification’, and ‘prediction’, we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing ‘things in the world’, these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research questions? (...) In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors. (shrink)

In the era of “big data,” science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of (...) applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use. After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

Surveillance studies scholars and privacy scholars have each developed sophisticated, important critiques of the existing data-driven order. But too few scholars in either tradition have put forward alternative substantive conceptions of a good digital society. This, I argue, is a crucial omission. Unless we construct new “sociotechnical imaginaries,” new understandings of the goals and aspirations digital technologies should aim to achieve, the most surveillance studies and privacy scholars can hope to accomplish is a less unjust version of the (...) technology industry’s own vision for the future. (shrink)

Mosquito-borne diseases represent a significant global disease burden, and recent outbreaks of such diseases have led to calls to reduce mosquito populations. Furthermore, advances in ‘gene-drive’ technology have raised the prospect of eradicating certain species of mosquito via genetic modification. This technology has attracted a great deal of media attention, and the idea of using gene-drive technology to eradicate mosquitoes has been met with criticism in the public domain. In this paper, I shall dispel two moral objections that have been (...) raised in the public domain against the use of gene-drive technologies to eradicate mosquitoes. The first objection invokes the concept of the ‘sanctity of life’ in order to claim that we should not drive an animal to extinction. In response, I follow Peter Singer in raising doubts about general appeals to the sanctity of life, and argue that neither individual mosquitoes nor mosquitoes species considered holistically are appropriately described as bearing a significant degree of moral status. The second objection claims that seeking to eradicate mosquitoes amounts to displaying unacceptable degrees of hubris. Although I argue that this objection also fails, I conclude by claiming that it raises the important point that we need to acquire more empirical data about, inter alia, the likely effects of mosquito eradication on the ecosystem, and the likelihood of gene-drive technology successfully eradicating the intended mosquito species, in order to adequately inform our moral analysis of gene-drive technologies in this context. (shrink)

I offer a meta-level analysis of realist arguments for the reliability of ampliative reasoning about the unobservable. We can distinguish form-driven and content-driven arguments for realism: form-driven arguments appeal to the form of inductive inferences, whilst content-driven arguments appeal to their specific content. After regimenting the realism debate in these terms, I will argue that the content-driven arguments are preferable. Along the way I will discuss how my analysis relates to John Norton’s recent, more general (...) thesis that the grounds for licit induction are always material. (shrink)

Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such (...) as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

The issue of the definition and position of archaeology as a discipline is examined in relation to the dispute which took place from 1980 to 2009 between the archaeologist Jean-Claude Gardin and the sociologist Jean-Claude Passeron. This case study enables us to explore the actual conceptual relationships between archaeology and the other sciences (as opposed to those wished for or prescribed). The contrasts between the positions declared by the two researchers and the rooting of their arguments in their disciplines are (...) examined: where the sociologist makes use of his philosophical training, the archaeologist relies mainly on his work on semiology and informatics. Archaeology ultimately plays a minor role in the arguments proposed. This dispute therefore cannot be considered as evidence for the movement of concepts between archaeology and the social sciences. A blind spot in the debate, relating to the ontological specificities of archaeological objects, nevertheless presents itself as a possible way of implementing this movement. (shrink)

Precision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to (...) address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided. (shrink)

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

This paper demonstrates the practical and philosophical strengths of adopting Luciano Floridi’s “general definition of information” (GDI) for use in the information sciences (IS). Many definitions of information have been proposed, but little work has been done to determine which definitions are most coherent or useful. Consequently, doubts have been cast on the necessity and possibility of finding a definition. In response to these doubts, the paper shows how items and events central to IS are adequately described by Floridi’s conception (...) of information, and demonstrates how it helps clarify the muddy theoretical framework resulting from the many previous definitions. To this end, it analyzes definitions, popular in IS, that conceive of information as energy, processes, knowledge, and physical objects. The paper finds that each of these definitions produces problematic or counterintuitive implications that the GDI suitably accounts for. It discusses the role of truth in IS, notes why the GDI is preferable to its truth-requiring variant, and ends with comments about the import of such a theory for IS research and practice. (shrink)

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 (...) males, 705 females) with 2,410 publications in the 2008 – 2018 period was extracted and analyzed. Various factors were considered including age, gender, new authors, leading authors, co-authorship, and Impact Factor. The findings suggest a high level of contribution from authors at the age of 40 – 44 in economics (858 publications) in a 12-years period, which is equivalent to the social medicine total output, and two times more than the total output of the education. Moreover, the presence and reinforcement of male researchers are still dominating in Economics and other fields, with the only exception of education. Despite the rapid rise in the number of Vietnamese lead authors, gender disparity among disciplines is an issue. Contrary to the strong international collaboration-oriented tendency in social medicine, economics, and other fields, educational authors are not open to international collaborating. Finally, most of the publications in economics belong to the group with JIF from 0 to 2, in contrast with the high number of social medicine publications with JIF from 2 to 5, which suggesting the field of economics is fulfilling the quantity, but still, need more quality publications. (shrink)

The boundaries of social categories are frequently altered to serve normative projects, such as social reform. Griffiths and Khalidi argue that the value-driven modification of categories diminishes the epistemic value of social categories. I argue that concerns over value-modified categories stem from problematic assumptions of the value-free ideal of science. Contrary to those concerns, non-epistemic value considerations can contribute to the epistemic improvement of a scientific category. For example, the early history of the category infantile autism shows how (...) non-epistemic value considerations can contribute to delimiting and establishing infantile autism as a distinct category in mainstream psychiatry. In the case of infantile autism, non-epistemic considerations have led to a new interpretation of existing data, the expansion of research to include biology, and the creation of diagnostic criteria that further contribute to collecting relevant data. Given this case study, it can be argued that non-epistemic values can improve our knowledge of scientific categories. (shrink)

Advancements in computing, instrumentation, robotics, digital imaging, and simulation modeling have changed science into a technology-driven institution. Government, industry, and society increasingly exert their influence over science, raising questions of values and objectivity. These and other profound changes have led many to speculate that we are in the midst of an epochal break in scientific history. -/- This edited volume presents an in-depth examination of these issues from philosophical, historical, social, and cultural perspectives. It offers arguments both (...) for and against the epochal break thesis in light of historical antecedents. Contributors discuss topics such as: science as a continuing epistemological enterprise; the decline of the individual scientist and the rise of communities; the intertwining of scientific and technological needs; links to prior practices and ways of thinking; the alleged divide between mode-1 and mode-2 research methods; the commodification of university science; and the shift from the scientific to a technological enterprise. Additionally, they examine the epochal break thesis using specific examples, including the transition from laboratory to real world experiments; the increased reliance on computer imaging; how analog and digital technologies condition behaviors that shape the object and beholder; the cultural significance of humanoid robots; the erosion of scientific quality in experimentation; and the effect of computers on prediction at the expense of explanation. -/- Whether these events represent a historic break in scientific theory, practice, and methodology is disputed. What they do offer is an important occasion for philosophical analysis of the epistemic, institutional and moral questions affecting current and future scientific pursuits. (shrink)

Was there a concept of data before the so-called ‘data revolution’? This paper contributes to the history of the concept of data by investigating uses of the term ‘data’ in texts of the Royal Society's Philosophical Transactions for the period 1665–1886. It surveys how the notion enters the journal as a technical term in mathematics, and charts how over time it expands into various other scientific fields, including Earth sciences, physics and chemistry. The paper argues that (...) in these texts the notion of data is not used merely as a rhetorical category, and also cannot strictly be identified with the category of evidence. Instead, the notion comes with an associated epistemic structure, one that is in line with its development from an early mathematical use. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not (...) a measure of its epistemic reliability. Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...) at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

At the beginning of the COVID-19 pandemic, high hopes were placed on digital contact tracing. Digital contact tracing apps can now be downloaded in many countries, but as further waves of COVID-19 tear through much of the northern hemisphere, these apps are playing a less important role in interrupting chains of infection than anticipated. We argue that one of the reasons for this is that most countries have opted for decentralised apps, which cannot provide a means of rapidly informing users (...) of likely infections while avoiding too many false positive reports. Centralised apps, in contrast, have the potential to do this. But policy making was influenced by public debates about the right app configuration, which have tended to focus heavily on privacy, and are driven by the assumption that decentralised apps are “privacy preserving by design”. We show that both types of apps are in fact vulnerable to privacy breaches, and, drawing on principles from safety engineering and risk analysis, compare the risks of centralised and decentralised systems along two dimensions, namely the probability of possible breaches and their severity. We conclude that a centralised app may in fact minimise overall ethical risk, and contend that we must reassess our approach to digital contact tracing, and should, more generally, be cautious about a myopic focus on privacy when conducting ethical assessments of data technologies. (shrink)

The word data (sing. datum) is originally Latin for “things given or granted”. Because of such a humble and generic meaning, the term enjoys considerable latitude both in its technical and in its common usage, for almost anything can be referred to as a “thing given or granted” (Cherry [1978]). With some reasonable approximation, four principal interpretations may be identified in the literature. The first three captures part of the nature of the concept and are discussed in the next (...) section. The fourth is the most fundamental and satisfactory, so it is discussed separately, in section three. On its basis, some further clarifications about the nature of data are introduced in sections four to six. A reminder about the social, legal and ethical issues raised by the use of data concludes this entry. (shrink)

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to (...) target a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...) central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

The thesis of theory-ladenness of observations, in its various guises, is widely considered as either ill-conceived or harmless to the rationality of science. The latter view rests partly on the work of the proponents of New Experimentalism who have argued, among other things, that experimental practices are efficient in guarding against any epistemological threat posed by theory-ladenness. In this paper I show that one can generate a thesis of theory-ladenness for experimental practices from an influential New Experimentalist account. The (...) notion I introduce for this purpose is the concept of ‘theory-driven data reliability judgments’, according to which theories which are sought to be tested with a particular set of data guide reliability judgments about those very same data. I provide various prominent historical examples to show that TDRs are used by scientists to resolve data conflicts. I argue that the rationality of the practices which employ TDRs can be saved if the independent support of the theories driving TDRs is construed in a particular way. (shrink)