Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential (...) ethical challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data (...) class='Hi'>science (machine learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data (...) class='Hi'>science (machine learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

The advancement in technology has changed the workflow and the role of human translator in recent years. The impact from the trend of technology-mediated translation prompted the ratification of technology literacy as a major competence for modern translators. Consequently, teaching of translation technology including but not limited to Computer-aided Translation and Machine Translation became part of comprehensive curricula for translation training programs. However, in many institutions, the teaching of translation technology was haunted by issues such as: narrow scope of curriculum (...) design, outdated technologies, and unbalance between theories and practices in teaching. The study was the pilot evaluation of a tailored course to foster translation trainees’ knowledge and abilities of data science. The course was designed to be a fundamental step toward sophisticated translation technologies. During the pilot evaluation of the 8-week course, 85 students were recruited as participants. The study adopted a mix-method design by employing a survey to investigate student’s level of satisfaction toward the course and focus group discussion to understand students’ attitudes and perceptions of key aspects of the course. By interpreting the results from statistical analysis of the survey and thematic analysis of the focus group discussion, the course of data science for translators was well received among participants. The evaluation project manifested the feasibility and effectiveness of a translator-oriented data science course. (shrink)

Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature (...) of data science as both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of ‘thoughtlessness’. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the ‘view from nowhere’ without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad’s idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of ‘machine learning for the people’. (shrink)

How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. (...) These negotiations laid the foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work. (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)

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to: (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modelling practices, particularly the recognition of transdisciplinary expertise as crucial (...) to developing and interpreting data models. Acknowledging such shifts in evidential, experimental and modelling practices helps avoid harmful applications of data-intensive methods. (shrink)

Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to (...) data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal science. (shrink)

Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data (...) science technologies. However, we argue that epistemic injustices can be distinguished conceptually from more familiar kinds of harm. We argue that epistemic harms are morally relevant even in cases where those who suffer them are unharmed in other ways. Via a series of examples from the criminal justice system, workplace hierarchies, and educational contexts we explain the kinds of epistemic injustice that can result from common uses of data science technologies. (shrink)

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts (...) of informational privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age. (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)

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to (...) opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement. (shrink)

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS (...) should deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed. (shrink)

Science and technology studies and the emerging field of data science share surprising elective affinities. At the growing intersections of these fields, there will be many opportunities and not a few thorny difficulties for STS scholars. First, I discuss how both fields frame the rollout of data science as a simultaneously social and technical endeavor, even if in distinct ways and for diverging purposes. Second, I discuss the logic of domains in contemporary computer, information, and (...) data science circles. While STS is often agnostic about the borders between the sciences or with industry and state—occasionally taking those boundaries as an object of study—data science takes those boundaries as its target to overcome. These two elective affinities present analytic and practical challenges for STS but also opportunities for engagement. Overall, in addition to these typifications, I urge STS scholars to strategically position themselves to investigate and contribute to the breadth of transformations that seek to touch virtually every science and newly bind spheres of academy, industry, and state. (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 this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data (...) for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding. (shrink)

The modern abundance and prominence of data have 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)

This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more (...) or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. (shrink)

Over the past 10 years, Cognitive Linguistics has taken a quantitative turn. Yet, concerns have been raised that this preoccupation with quantification and modelling may not bring us any closer to understanding how language works. We show that this objection is unfounded, especially if we rely on modelling techniques based on biologically and psychologically plausible learning algorithms. These make it possible to take a quantitative approach, while generating and testing specific hypotheses that will advance our understanding of how knowledge of (...) language emerges from exposure to usage. (shrink)

As disparities and data science researchers, we write in response to Char and colleagues paper on “Identifying Ethical Considerations for Machine Learning Healthcare Applications.” While the...

Purpose Underlying much recent development in data science and artificial intelligence (AI) is a dependence on the labour of precarious crowdworkers via platforms such as Amazon Mechanical Turk. These platforms have been widely critiqued for their exploitative labour relations, and over recent years, there have been various efforts by academic researchers to develop interventions aimed at improving labour conditions. The aim of this paper is to explore US-based crowdworkers’ views on two proposed interventions: a browser plugin that detects (...) automated quality control “Gold Question” (GQ) checks and a proposal for a crowdworker co-operative. Design/methodology/approach The authors interviewed 20 US-based crowdworkers and undertook a thematic analysis of collected data. Findings The findings indicate that US-based crowdworkers tend to have negative and mixed feelings about the GQ detector, but were more enthusiastic about the crowdworker co-operative. Originality/value Drawing on theories of precarious labour, this study suggests an explanation for the findings based on US-based workers’ objective and subjective experiences of precarity. The authors argue that for US-based crowdworkers “constructive” interventions such as a crowdworker co-operative have more potential to improve labour conditions. (shrink)

Name der Zeitschrift: Cognitive Linguistics Jahrgang: 27 Heft: 4 Seiten: 507-526.

In the past few years, scholars have been questioning whether the current approach in data ethics based on the higher level case studies and general principles is effective. In particular, some have been complaining that such an approach to ethics is difficult to be applied and to be taught in the context of data science. In response to these concerns, there have been discussions about how ethics should be “embedded” in the practice of data science, (...) in the sense of showing how ethical issues emerge in small technical choices made by data scientists in their day-to-day activities, and how such an approach can be used to teach data ethics. However, a precise description of how such proposals have to be theoretically conceived and could be operationalized has been lacking. In this article, we propose a full-fledged characterization of ‘embedding’ ethics, and how this can be applied especially to the problem of teaching data science ethics. Using the emerging model of ‘microethics’, we propose a way of teaching daily responsibility in digital activities that is connected to the higher level ethical challenges discussed in digital/data ethics. We ground this microethical approach into a virtue theory framework, by stressing that the goal of a microethics is to foster the cultivation of moral virtues. After delineating this approach of embedding ethics in theoretical detail, this article discusses a concrete example of how such a ‘micro-virtue ethics’ approach could be practically taught to data science students. (shrink)

In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue (...) theory tradition, in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. (shrink)

Compared to existing classical approaches to semiotics which are dyadic (signifier/signified, F. de Saussure) and triadic (symbol/concept/object, Ch. S. Peirce), this theory can be characterized as tetradic ([sign/semion]//[object/noema]) and is the result of either doubling the dyadic approach along the semiotic/ordinary dimension or splitting the ‘concept’ of the triadic one into two (semiotic/ordinary). Other important features of this approach are (a) the distinction made between concepts (only functional pairs of extent and intent) and categories (as representations of expressions) and (b) (...) the indication of the need for providing the mathematical passage from the duality between two sets (where one is a singleton) within systems of sets to category-theoretical monoids within systems of categories while waiting for the solution of this problem in the field of logic. Last but not least, human language expressions are the most representative physical instances of semiotic objects. Moreover, as computational experiments which are possible with linguistic objects present a high degree of systematicity (of oppositions), in general, it is relatively easy to elucidate their dependence on the concepts underlying signs. This new semiotic theory or rather this new research program emerged as the fruit of experimentation and reflection on the application of data science tools elaborated within the frameworks of Rough Set Theory (RST), Formal Context Analysis (FCA) and, though only theoretically, Distributed Information Logic (DIL). The semiotic objects (s-objects) of this theory can be described in tabular datasets. Nevertheless, at this stage of formalisation of the theory, lattices (not trees) can be used as working representation structures for characterizing the components of concept systems and graphs for categories of each layer. (shrink)

For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on the challenges that arise when working to establish this interdisciplinary environment. We discuss how (...) we perceive the differences and commonalities of the questions we ask to understand digital behaviors, and how our methods may complement each other. Finally, we describe what a path toward common ground between these fields looks like when viewed from data science. (shrink)

In this study, I examine the institutional model of data science as a nascent profession undergoing an occupational founding phase. Drawing on interviews with sixty data scientists, senior managers, and professors from Israel as well as observations at the local professional community’s events, I argue that data scientists endorse an open institutional model, upholding largely internet-based institutions focusing on knowledge sharing, networking, and collaboration. This model grants data scientists expertise, autonomy, and authority vis-à-vis clients, employers, (...) and states; provides them with continued credentialing independent of employing organizations; encourages the wide entry of new members; and helps them deal with the accelerated temporality of their field. This open model enables an omnivorous spreading of data science expertise and is used to challenge professionalization as an occupational-institutional model in other professions. Still, this model faces many challenges. (shrink)

This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. -/- Causation in Population Health Informatics and Data Science provides a detailed (...) guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics. (shrink)

In the expansion of health ecosystems, issues of responsibility and sustainability of the data science involved are central. The idea that these values should be central to the practice of data science is increasingly gaining traction, yet there is no agreement on what exactly makes data science responsible or sustainable because these concepts prove slippery when applied to a global field involving commercial, academic and governmental actors. This lack of clarity is causing problems in (...) setting goals and boundaries for data scientific practice, and risks fundamental disagreement on governance principles for this emerging field. We will argue in this commentary for a commons analytical framework as one approach to this problem, since it offers useful signposts for how to establish governance principles for shared resources. (shrink)

The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to (...) produce Big Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate. (shrink)

In this chapter, we propose a non-traditional RCR training in data science that is grounded in a virtue theory framework. First, we delineate the approach in more theoretical detail by discussing how the goal of RCR training is to foster the cultivation of certain moral abilities. We specify the nature of these ‘abilities’: while the ideal is the cultivation of virtues, the limited space allowed by RCR modules can only facilitate the cultivation of superficial abilities or proto-virtues, which (...) help students to familiarize themselves with moral and political issues in the data science environment. Third, we operationalize our approach by stressing that (proto-)virtue acquisition (like skill acquisition) occurs through the technical and social tasks of daily data science activities, where these repetitive tasks provide the opportunities to develop (proto-)virtue capacity and to support the development of ethically robust data systems. Finally, we discuss a concrete example of implementing this approach. In particular, we describe how this method is applied to teach data ethics to students participating in the CODATA-RDA Data Science Summer Schools. (shrink)

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its (...) critics can and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We (...) suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science. (shrink)

Recently, philosophers and social scientists have turned their attention to the epistemological shifts provoked in established sciences by their incorporation of big data techniques. There has been less focus on the forms of epistemology proper to the investigation of algorithms themselves, understood as scientific objects in their own right. This article, based upon 12 months of ethnographic fieldwork with Russian data scientists, addresses this lack through an investigation of the specific forms of epistemic attention paid to algorithms by (...) data scientists. On the one hand, algorithms are unlike other mathematical objects in that they are not subject to disputation through deductive proof. On the other hand, unlike concrete things in the world such as particles or organisms, algorithms cannot be installed as the objects of experimental systems directly. They can only be evaluated in their functioning as components of extended computational assemblages; on their own, they are inert. As a consequence, the epistemological coding proper to this evaluation does not turn on truth and falsehood but rather on the efficiency of a given algorithmic assemblage. This article suggests that understanding the forms of algorithmic rationality employed in such inquiry is crucial for charting the place of data science within the contemporary academy and knowledge economy more generally. (shrink)

As big data techniques become widespread in journalism, both as the subject of reporting and as newsgathering tools, the ethics of data science must inform and be informed by media ethics. This article explores emerging problems in ethical research using big data techniques. It does so using the duty-based framework advanced by W.D. Ross, who has significantly influenced both research science and media ethics. A successful framework must provide stability and flexibility. Without stability, ethical precommitments (...) will vanish as technology rapidly shifts costs. Without flexibility, traditional approaches will rapidly become obsolete in the face of technological change. The article concludes that Ross's duty-based approach both provides stability in the face of rapid technological change and flexibility to innovate to achieve the original purpose of basic ethical principles. (shrink)

This paper examines classification practices in the domain of plant data semantics, and particularly methods used to label plant traits to foster the collection, management, linkage and analysis of data about crops across locations—which crucially inform research and interventions on plants and agriculture. The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants: particularly the biological, cultural, scientific and semantic (...) diversity affecting the identification and description of plant traits, the methods used to generate and process data, and the goals and skills of those with relevant expertise—including farmers and breeders. Through a study of the Crop Ontology (which explicitly recognizes and negotiates diversity) and its application to cassava breeding, I argue for a process-sensitive approach to the naming of plant traits that focuses on documenting environmental processes instead of biological products. I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions. I conclude that the study of data semantics and related descriptors constitutes a productive and underexplored way to think about the epistemic import of naming traits within plant science. The effort to articulate semantic differences among plant varieties and methods of data processing can generate newly inclusive ways to develop and communicate biological knowledge. In turn, such practices have the potential to defy existing understandings of systematisation and hierarchies of expertise in biology, thus bolstering the extent to which plant science can support biodiversity and sustainable agriculture. (shrink)

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

The era of ‘big data’ studies and computational social science has recently given rise to a number of realignments within and beyond the social sciences, where otherwise distinct data formats – digital, numerical, ethnographic, visual, etc. – rub off and emerge from one another in new ways. This article chronicles the collaboration between a team of anthropologists and sociologists, who worked together for one week in an experimental attempt to combine ‘big’ transactional and ‘small’ ethnographic data (...) formats. Our collaboration is part of a larger cross-disciplinary project carried out at the Danish Technical University, where high-resolution transactional data from smartphones allows for recordings of social networks amongst a freshman class. With a parallel deployment of ethnographic fieldwork among the DTU students, this research set-up raises a number of questions concerning how to assemble disparate ‘data-worlds’ and to what epistemological and political effects? To address these questions, a specific social event – a lively student party – was singled out from the broader DTU dataset. Our experimental collaboration used recordings of Bluetooth signals between students’ phones to visualize the ebb and flow of social intensities at the DTU party, juxtaposing these with ethnographic field-notes on shifting party atmospheres. Tracing and reflecting on the process of combining heterogeneous data, the article offers a concrete case of how a ‘stitching together’ of digital and ethnographic data-worlds might take place. (shrink)

Over the last decade, ‘data scientists’ have burst into society as a novel expert role. They hold increasing responsibility for generating and analysing digitally captured human experiences. The article considers their professionalization not as a functionally necessary development but as the outcome of classification practices and struggles. The rise of data scientists is examined across their discursive classification in the academic and economic fields in both the USA and Germany. Despite notable differences across these fields and nations, the (...) article identifies two common subjectivation patterns. Firstly, data scientists are constructed as hybrids, who combine generally conflictive roles as both generalists and specialists; technicians and communicators; data exploiters and data ethicists. This finding is interpreted as demonstrating a discursive distinction between data scientists and other competing and supposedly more one-dimensional professionals, such as statisticians or computer scientists. Secondly, the article uncovers a discursive construction that interpellates data scientists as discoverers of needs. They are imagined as explorative work subjects who can establish growth for digital capitalism by generating behavioural patterns that allow for personalization, customization and optimization practices. (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)

Many studies in big data focus on the uses of data available to researchers, leaving without treatment data that is on the servers but of which researchers are unaware. We call this dark data, and in this article, we present and discuss it in the context of high-performance computing facilities. To this end, we provide statistics of a major HPC facility in Europe, the High-Performance Computing Center Stuttgart. We also propose a new position tailor-made for coping (...) with dark data and general data management. We call it the scientific data officer and we distinguish it from other standard positions in HPC facilities such as chief data officers, system administrators, and security officers. In order to understand the role of the SDO in HPC facilities, we discuss two kinds of responsibilities, namely, technical responsibilities and ethical responsibilities. While the former are intended to characterize the position, the latter raise concerns—and proposes solutions—to the control and authority that the SDO would acquire. (shrink)

Through a critical analysis of recent developments in the theory and practice of data science, including nascent feminist approaches to data collection and analysis, this commentary aims to signal the need for a transnational feminist orientation towards data science. I argue that while much needed in the context of persistent algorithmic oppression, a Western feminist lens limits the scope of problems, and thus—solutions, critical data scholars, and scientists can consider. A resolutely transnational feminist approach (...) on the other hand, can provide data theorists and practitioners with the hermeneutic tools necessary to identify and disrupt instances of injustice in a more inclusive and comprehensive manner. A transnational feminist orientation to data science can pay particular attention to the communities rendered most vulnerable by algorithmic oppression, such as women of color and populations in non-Western countries. I present five ways in which transnational feminism can be leveraged as an intervention into the current data science canon. (shrink)

This article employs a cybernetic approach to explore the scope of what constitutes anthropological and ethnographic research and the potential to utilize data science techniques to broaden what constitutes ethnography. Four types of relationships anthropologists historically have tended to seek out with data science as a discipline: anthropology of data science, anthropology over data science, anthropology with data science and, the least developed of the four, anthropology by data (...) class='Hi'>science. I relate potential insights data scientists have cultivated on abductive, bottom-up quantitative research that might be useful for anthropologists in particular and cybernetically minded thinkers in general. Grounded Nick Seaver’s concept of bastard disciplines and methodologies, an anthropology by data science relationship provides a beneficial way to ground such strategic incorporations within anthropological research and helpful food for thought for cybernetic scholars in other disciplinary contexts. (shrink)

Philosophy Compass, Volume 17, Issue 1, January 2022.