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Critical data studies: An introduction:

Critical data studies: An introduction: Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. Keywords Critical Data Studies, Big Data, data science, data ethics, data subjects postpositivist and critical approaches (Kitchin, 2015). Introduction Discourses and practices surrounding the Big Data Data are a form of power. Organizations own vast quan- revolution (Mayer-Schonberger and Cukier, 2013) tities of user information and hold lucrative data capital feature an emerging variety of new and inventive data (Yousif, 2015), wield algorithms and data processing science techniques that seek to further scientific inquiry tools with the ability to influence emotions and culture by collecting large amounts of data, directing research- (Gillespie, 2014; Kramer et al., 2016; Striphas, 2015), ers to novel observations and findings. Some arguments and researchers invoke data in the name of scientific in favor of data-intensive studies situate Big Data objectivity while often ignoring that data are never raw science as capable of overthrowing theory (Anderson, but always ‘‘cooked’’ (Gitelman, 2013). There is evidence 2008) and providing fine-grained analyses that no that data are surreptitiously extracted from data subjects longer require the critical eye of postpositivist thinking. (Hauge et al., 2016; Metcalf and Crawford, 2016), Yet, as some have noted, ‘‘Big Data’’ remains a meta- hijacked to serve agendas that benefit research and phor for a set of practices (Puschmann and Burgess, industry (Ioannidis, 2005, 2016), and compromised by 2014) that are in need of a critical ethos to problematize the interests of not only powerful business organizations inherent assumptions about data that pervade current but also hackers and rogue agents (Coleman, 2014; discourses in the natural and social sciences. Big Data Elmer et al., 2015). While data are all of the above are connected to the world in a variety of contexts that and more, they are also conspicuous in their absence—a exist ‘‘beyond’’ the realm of traditional data science. lack of data is another indication of power, the power not to look or to remain hidden (Brunton and Nissenbaum, 2015; Flyverbom et al., 2016). In their pres- University of Ontario Institute of Technology, Canada ence and absence, data are always-already active and University of Amsterdam, The Netherlands never neutral, part of an information geography (Graham, 2014, 2015) that is always in flux. Corresponding author: Current research trends in the social and natural Andrew Iliadis, University of Ontario Institute of Technology, 2000 sciences indicate a general prioritization of data- Simcoe St. N., Oshawa, ON L1H 7K4, Canada. intensive and positivistic approaches over long-held Email: andrew.iliadis@uoit.ca Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http:// www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access- at-sage). 2 Big Data & Society Big Data must remain open to cultural, ethical, and How do Big Data inflect and interact with society, critical perspectives, particularly when viewed as a social processes, and how we come to measure and modern archive of data facts and data fictions. Data, interact with them? along with its sciences and infrastructures, are informed The nascent field of CDS is a formal attempt at by specific histories, ideologies, and philosophies that naming the types of research that interrogate all tend to remain hidden, though there have been recent forms of potentially depoliticized data science and to calls for inquiry into these domains (Beer, 2016; boyd track the ways in which data are generated, curated, and Crawford, 2012; Crawford et al., 2014; Floridi, and how they permeate and exert power on all 2011; Kitchin, 2014, 2015). Further, issues of causality manner of forms of life. In what is already a classic (Illari and Russo, 2014), quality (Floridi and Illari, text, boyd and Crawford (2012) proposed a key set of 2014), security (Taddeo, 2013; Taddeo and Floridi, critical questions for Big Data. Going further, 2015), and uncertainty (Leonelli, 2015) continue to pro- Crawford et al. (2014) edited a special collection that voke debate among Big Data researchers, practitioners, built on those original questions by provoking new and their critics. As the product of multiple sites of inquiries into Big Data critique, including issues related work, layered analytic techniques, experimental prac- to politics, ethics, and epistemology. Dalton and tices, and various competing discourses, Big Data are Thatcher (2014) made the original call for CDS and susceptible to losing provenance and their ability to be provided the first explicit reference to the field by ‘‘about’’ only one thing, their origins and interpret- asking ‘‘what does a critical data studies look like?’’ ations becoming multiple and conflicting as metadata Kitchin and Lauriault (2014) offered an answer to are mixed with primary, secondary, and derivative Dalton and Thatcher’s question and proposed that data. Such a confluence of data sources and meanings CDS should study ‘‘data assemblages,’’ that is ‘‘the inevitably leads to data disorder, the potential for harm technological, political, social and economic appara- to data subjects, and the need for strong ethical inves- tuses and elements that constitutes and frames the tigations into data and its discontents. Big Data belong generation, circulation and deployment of data’’ (1). to a web of subjects, institutions, texts, and authors that Before and after those publications, CDS has covered tend to remain invisible to researchers who prefer to a wide area of communications inquiry, including data treat Big Data science as a new form of positivism—but power issues in social media, apps, the Internet, web, the ‘‘data’’ of ‘‘Big Data’’ are not always the whole and platforms, but also and equally importantly statis- story. As Foucault famously put it in The Archaeology tics, policy, research, and organization. In every way of Knowledge, the figures that populate a field do not that data are organized in a communicative context, communicate only by the logical successions of prop- CDS—as a clear call for the critical investigation of ositions but also by the ‘‘positivity of their discourse’’ Big Data science—has coalesced around researchers which defines a field where ‘‘formal identities, thematic ready to deploy pronounced critical frameworks in continuities, translations of concepts, and polemical order to foreground data’s power structures. Of interchanges may be deployed’’ (2002: 143). Similarly, course, such a field understandably runs the risk of Big Data must be challenged by acknowledging the being overly broad and presumptuously inclusive. limitations of the positivity of its discourse and the As Dalton et al. (2016) note, CDS might offend realities of the shifting information infrastructures researchers who point out that all forms of research (Bowker et al., 2010), multiple data subjects and their are critical and create a false separation between critical rights (Haraway, 1991; Jones, 2016), deep information theory and data science. As such, CDS continues to histories (Beniger, 1989), work and power (Zuboff, remain an inclusive field that is open to self-critique 1988), and hybrid digital cultures (Striphas, 2016) that and dialog, itself politicized in its quest to politicize underpin it. Big Data. At the very least, the amorphous groups of individuals, texts, projects, and institutions that seek a specific and pronounced critical engagement with Big Critical data studies (CDS) Data science now have a name to use. One way that a critical approach to Big Data contrib- The multidimensionality of possible critiques of Big utes to knowledge is by helping define the questions Data science grows out of the plurality of data them- that inform epistemological frameworks around social selves. In their ability to provide interpretations of real- ity, data are apprehended through various levels of issues related to data. A critical approach to Big Data investigates meta-theoretical modes of conversation informational abstraction (Floridi, 2011) that frame and styles of scientific thinking (Hacking, 1994) that what data are about. Such frameworks and perspectives pervade data science—it does not contribute to know- on Big Data are multiple and diverse and may attend ledge in the positivistic sense but instead analyzes the to any number of apparatuses that reflect specific sub- ground upon which positivistic Big Data science stands. ject positions. Levels of informational abstraction—the Iliadis and Russo 3 product of positionalities that constrain and afford mainly to impede science-based policy processes what data can be about—are a gateway into the mul- weaponize the concept of data transparency’’ (1). tiple roles that data play and the ways that abstraction Similarly, work in CDS should be attuned to the differ- may be adopted, manipulated, or repurposed for any ent ways that data can be hijacked and/or weaponized number of aims. Choosing a level of abstraction from to substantiate pseudoscientific claims that belie polit- which to view Big Data alters the types of conversations ical motivations. that can be had about data, its aims, and functions. Bronson and Knezevic (2016) take up the issue of As noted by Kitchin (2014) and Kitchin and Big Data in food and agriculture. They review data Lauriault (2014), the subjects of CDS are the sociotech- applications in the agricultural food sector and note nical ‘‘data assemblages’’ that make up Big Data. The that such analytics tools have ‘‘implications for rela- apparatus and elements of a data assemblage may tionships of power between players in the food system include systems of thought, forms of knowledge, (e.g., between farmers and large corporations)’’ (1), finance, political economy, governmentalities and leg- looking at issues such as data ownership in the context alities, materialities and infrastructures, practices, of applications like Monsanto’s Weed ID app, as well organizations and institutions, subjectivities and com- as the privacy implications of John Deere’s precision munities, places, and the marketplace where data are agricultural equipment. CDS works in food and agri- constituted. Assemblages should be understood as culture platforms tend to be less visible compared to structures that emerge as constitutive of Big Data, their more traditional social media counterparts. viewed from a variety of social positions at multiple As such, CDS calls for the critical investigation of scales (local, national, international) that exert power. data-intensive fields that exist outside the ken of trad- Data assemblages are the powerful complex of entities itional ‘‘media theory’’ literature, such as food and that form the underlying production of Big Data sci- agriculture processing data. While traditional social ence at multiple levels of abstraction and in a plurality media platforms such as Facebook and Twitter must of domains. remain open to CDS work, CDS must further attend to critical data problems in multiple data science domains, from data science’s use in food and agricul- CDS work ture to Big Data techniques in environmental and So far, CDS has emerged as a loose knit group of financial regulation. frameworks, proposals, questions, and mani- Dalton et al. (2016) offer a welcome and open dialog festos—something to be expected of fields still in their on data, time, and space. Their contribution takes the infancy. What need to be established are long-term pro- form of a three-way interview (a valuable format that jects that take up specific challenges in CDS by propos- should be used more often). CDS originated in the field ing critical investigations into Big Data assemblages. In of geography and this article builds on Dalton and this Big Data & Society CDS special theme, we collect a Thatcher’s (2014) original call for CDS work that group of articles that seek to build on the earlier work focuses on problems deeply connected to locality and of CDS researchers to expose Big Data science prob- identity. Issues related to the Big Data divide, data lems in social contexts. These articles and commen- discourses, data subjects, and data corporations are taries deal with a variety of themes and issues related framed as central to CDS. Dalton et al. discuss ‘‘the to Big Data science, ranging from food and health to stakes, ideas, responsibilities, and possibilities of critical policing and the environment. The articles feature data studies’’ (1) and in doing so continue the practice many issues and subjects that have been of concern to of open, sensitive, and politicized dialog among CDS CDS researchers and offer a glimpse into the types of researchers. As a plural and multifaceted field of scholarship that CDS work might continue to produce inquiry, CDS should continue to be open to such with renewed attention. forms of dialog, self-critique, and coinvestigation. For example, Levy and Johns (2016) note that Big Moving from self-critique to a critical engagement of Data can be counterintuitively ‘‘weaponized’’ under the governmental data practices, Rieder and Simon (2016) veil of openness and transparency and responsible data discuss data’s influence on truth and objectivity in the practices. While Levy and Johns generally agree with science of governance. They highlight a growing inter- data safety practices, they argue that ‘‘legislative efforts est in evidenced-based policy-making and provide an account of data-driven forms of governance. Should that invoke the language of data transparency can sometimes function as ‘Trojan Horses’ through which numerical evidence produced by Big Data science other political goals are pursued’’ (1). Through an serve as mandate for the production of policy and investigation of the ‘‘sound science’’ initiatives of the new forms of governance? Such questions are beginning 1990s and current efforts to open environmental data to to be addressed among CDS researchers who look to public inspection, they find that ‘‘[r]ules that exist interrogate the ways Big Data are used to support 4 Big Data & Society changes in governmentality and social organization, as surrounding the use of Big Data in humanitarian well as issues related to social policy and practices. contexts. Another policy issue that should be of interest to Beyond humanitarian social data problems, CDS scholars is the use of human subjects in research. sociotechnical systems that populate the worlds of eco- Metcalf and Crawford (2016) address the fundamen- nomics, finance, and the stock market pose a signifi- tally important question of ‘‘Where are human subjects cant challenge to CDS due to their closed, inaccessible in Big Data research?’’ In discussing the emerging nature. Further, semiautomated systems like the stock ethics divide, Metcalf and Crawford chart what they market and high frequency trading pose new questions view as the ‘‘growing discontinuities between the in terms of data subjects and subjectivity. Christiaens research practices of data science and established (2016) provides a critical inquiry into digital subjecti- tools of research ethics regulation’’ (1). Making the vation in the world of finance, writing that ‘‘traders claim that certain features of ethics regulations have been steadily integrated into computerized data cannot be adequately transferred from biomedical assemblages, which calls for an ontology that elimin- research to data science research, Metcalf and ates the distinction between human sovereign subjects Crawford find that this has led some data science and non-human instrumental objects’’ (1). Building on researchers to eschew ethical considerations relating the work of Maurizio Lazzarato, Christiaens provides to data subjects. Their article discusses current debates a critical take on human–machine interaction, arguing around the USA’s Common Rule regarding the regu- that the high-speed data-driven nature of financial lation of human subjects research and investigates the markets subjectivize traders in preconscious ways due regulation of social science research, arguing that ‘‘data to their inability to keep apace with automated trans- science should be understood as continuous with social actions. Christiaens argues that CDS must consider sciences in this regard’’ (1). In emphasizing the ethical processes of digital subjectivation and subjugation dimensions of public datasets and their subjects, that occur when Big Data science is applied to socio- Metcalf and Crawford call attention to a growing prob- technical systems that are governed by humans and lem in Big Data science. machines. Moving from data subjects to data places, Perng The theme of subjectivity is raised throughout these et al. (2016) take up the question of locative media papers in part due to the lack of discussion around human subjects in Big Data research. Perhaps the and data-driven computing experiments. They note the various ways in which ‘‘exploratory data-driven most vulnerable, minority and lower socioeconomic computing experiments’’ that use geocoding ‘‘seek to status subjects are affected by Big Data science in find ways to extract value and insight’’ (1) and raise often invisible and unforeseen ways. Currie et al. the concern that such practices often begin from data (2016) provide an example of such a case in their ana- rather than from theory. They argue that locative lysis of four datasets containing police officer-involved media data and computing experiments attempt to homicide statistics in Los Angeles. Their paper frames derive possible futures while having unintended conse- ‘‘police officer-involved homicide data as a rhetorical quences. They further argue that ‘‘using computing tool that can reify certain assumptions about the experiments to imagine potential urban futures pro- world and extend regimes of power’’ (1). Civic data, duces effects that often have little to do with creating they argue, can be incorporated into creative commu- new urban practices’’ (1). Rather, Perng et al. note that nity practice and events as a form of datactivism. such experiments serve to promote Big Data science Comparing local, regional, and national datasets on and the notion that data may be repurposed. police officer-involved homicides in Los Angeles, the Tackling another side of Big Data science and its authors provide ‘‘accounts of the semantics, granular- relationship to different localities, Mulder et al. (2016) ity, scale and transparency’’ of the data before des- look into the growing issue of crowdsourced crisis data cribing a ‘‘counter data action’’ (1) event held with and humanitarian work. Their aim is to investigate community members. whether Big Data can contribute to an inclusive Whether subjects can trust Big Data is a reoccurring humanitarian response during large crises. They argue concern and Symons and Alvarado (2016) take up this that Big Data are ‘‘socially constructed artefacts that question. Applying philosophy of science to software, reflect the contexts and processes of their creation’’ (1) Symons and Alvarado address some of the epistemo- across local and international contexts and analyze Big logical challenges posed by Big Data while addressing Data-making processes in the context of the 2010 Haiti the topics of computational modeling and simulation. and 2015 Nepal earthquakes. They find that ‘‘locally The authors take up the issue of ‘‘epistemic opacity’’ based, affected people [...] are marginalized in their while investigating the problem of error management ability to benefit from Big Data in support of their and error detection. Paying special attention to the own means’’ (1). As such, their work adds to debates relationship between error and path complexity in Iliadis and Russo 5 software, the article provides an overview of statistical derivative nature of online metadata in terms of meta- methods and reviews their limitations. data’s ability to potentially identify human users. The Finally, the CDS special theme concludes with two identification of social data problems should pair articles that critically examine the use of data derived Big Data science with common problems, allowing from computational modeling for epidemiology and the researchers to consider the shared nature of a problem- study of environmental pollution. Canali (2016) atic and to formulate ways in which it might be com- addresses the complicated issue of data-driven science’s monly articulated. This is not a transparent process and limitations and connection to causality. He focuses researchers should give ample thought to articulating on Big Data and causal knowledge by examining problematic scenarios involving social data. Critical EXPOsOMICS, a European Commission-funded pro- framework designs include viewing data as interpretive ject aiming to improve understanding of the relation and rhetorical assemblages in the construction of between exposure and disease. Canali shows how science, institutions, and citizens. Established critical causal knowledge is necessary for EXPOsOMICS and frameworks in CDS such as those oriented around argues that ‘‘data-driven claims about causality are data assemblages are just some of the possible direc- fundamentally flawed’’ (1), suggesting that causal tions for CDS frameworks, though it should not be knowledge must remain a necessary part of Big Data forgotten that CDS also consist of forms of datactivism science. Thoreau (2016) examines the use of computa- and should contribute to data literacy and data justice. tional models and their data to determine environmen- The application of social solutions to increase data lit- tal toxicity and assist in the regulation of chemicals. eracy and justice involves effecting change by conduct- They conclude that quantitative structure-activity ing research and sharing that research and the activities relationship models as causal explanation should be that might grow out of it with the public. Importantly, reconsidered by regulators. CDS should provide individuals with the necessary tools for becoming more informed and the ability to organize efforts around data justice issues. By main- Orientations and principles taining these orientations and principles, CDS should Each of the articles in this Big Data & Society special encourage us to think about Big Data science in terms theme share core concerns that we view as important to of the common good and social contexts. CDS. We will end by summarizing three general orien- tations and principles. Declaration of conflicting interests In his Nicomachean Ethics, Aristotle famously refers The author(s) declared no potential conflicts of interest with to an ‘‘education for the common good’’—a perspective respect to the research, authorship, and/or publication of this that can nurture care by encouraging a shared under- article. standing of specialized knowledge while emphasizing the importance of collective learning and interaction. Funding The notion of education for the common good deeply The author(s) received no financial support for the research, informs CDS frameworks which should be built to inte- authorship, and/or publication of this article. grate participatory learning and research. In our view, CDS follows three basic principles derived from this broadly Aristotelean approach: the identification of Notes social data problems, the design of critical frameworks 1. This Big Data & Society special theme on CDSs grew out for addressing social data problems, and the applica- of the Society for the Philosophy of Information’s Seventh tion of social solutions to increase data literacy. These Workshop, ‘‘Conceptual Challenges of Data in Science three simple principles allow for a collective learning and Technology’’ (2015, University College London). experience where critical approaches can be put to use http://www.socphilinfo.org/ in specific contexts. 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Critical data studies: An introduction:

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Abstract

Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. Keywords Critical Data Studies, Big Data, data science, data ethics, data subjects postpositivist and critical approaches (Kitchin, 2015). Introduction Discourses and practices surrounding the Big Data Data are a form of power. Organizations own vast quan- revolution (Mayer-Schonberger and Cukier, 2013) tities of user information and hold lucrative data capital feature an emerging variety of new and inventive data (Yousif, 2015), wield algorithms and data processing science techniques that seek to further scientific inquiry tools with the ability to influence emotions and culture by collecting large amounts of data, directing research- (Gillespie, 2014; Kramer et al., 2016; Striphas, 2015), ers to novel observations and findings. Some arguments and researchers invoke data in the name of scientific in favor of data-intensive studies situate Big Data objectivity while often ignoring that data are never raw science as capable of overthrowing theory (Anderson, but always ‘‘cooked’’ (Gitelman, 2013). There is evidence 2008) and providing fine-grained analyses that no that data are surreptitiously extracted from data subjects longer require the critical eye of postpositivist thinking. (Hauge et al., 2016; Metcalf and Crawford, 2016), Yet, as some have noted, ‘‘Big Data’’ remains a meta- hijacked to serve agendas that benefit research and phor for a set of practices (Puschmann and Burgess, industry (Ioannidis, 2005, 2016), and compromised by 2014) that are in need of a critical ethos to problematize the interests of not only powerful business organizations inherent assumptions about data that pervade current but also hackers and rogue agents (Coleman, 2014; discourses in the natural and social sciences. Big Data Elmer et al., 2015). While data are all of the above are connected to the world in a variety of contexts that and more, they are also conspicuous in their absence—a exist ‘‘beyond’’ the realm of traditional data science. lack of data is another indication of power, the power not to look or to remain hidden (Brunton and Nissenbaum, 2015; Flyverbom et al., 2016). In their pres- University of Ontario Institute of Technology, Canada ence and absence, data are always-already active and University of Amsterdam, The Netherlands never neutral, part of an information geography (Graham, 2014, 2015) that is always in flux. Corresponding author: Current research trends in the social and natural Andrew Iliadis, University of Ontario Institute of Technology, 2000 sciences indicate a general prioritization of data- Simcoe St. N., Oshawa, ON L1H 7K4, Canada. intensive and positivistic approaches over long-held Email: andrew.iliadis@uoit.ca Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http:// www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access- at-sage). 2 Big Data & Society Big Data must remain open to cultural, ethical, and How do Big Data inflect and interact with society, critical perspectives, particularly when viewed as a social processes, and how we come to measure and modern archive of data facts and data fictions. Data, interact with them? along with its sciences and infrastructures, are informed The nascent field of CDS is a formal attempt at by specific histories, ideologies, and philosophies that naming the types of research that interrogate all tend to remain hidden, though there have been recent forms of potentially depoliticized data science and to calls for inquiry into these domains (Beer, 2016; boyd track the ways in which data are generated, curated, and Crawford, 2012; Crawford et al., 2014; Floridi, and how they permeate and exert power on all 2011; Kitchin, 2014, 2015). Further, issues of causality manner of forms of life. In what is already a classic (Illari and Russo, 2014), quality (Floridi and Illari, text, boyd and Crawford (2012) proposed a key set of 2014), security (Taddeo, 2013; Taddeo and Floridi, critical questions for Big Data. Going further, 2015), and uncertainty (Leonelli, 2015) continue to pro- Crawford et al. (2014) edited a special collection that voke debate among Big Data researchers, practitioners, built on those original questions by provoking new and their critics. As the product of multiple sites of inquiries into Big Data critique, including issues related work, layered analytic techniques, experimental prac- to politics, ethics, and epistemology. Dalton and tices, and various competing discourses, Big Data are Thatcher (2014) made the original call for CDS and susceptible to losing provenance and their ability to be provided the first explicit reference to the field by ‘‘about’’ only one thing, their origins and interpret- asking ‘‘what does a critical data studies look like?’’ ations becoming multiple and conflicting as metadata Kitchin and Lauriault (2014) offered an answer to are mixed with primary, secondary, and derivative Dalton and Thatcher’s question and proposed that data. Such a confluence of data sources and meanings CDS should study ‘‘data assemblages,’’ that is ‘‘the inevitably leads to data disorder, the potential for harm technological, political, social and economic appara- to data subjects, and the need for strong ethical inves- tuses and elements that constitutes and frames the tigations into data and its discontents. Big Data belong generation, circulation and deployment of data’’ (1). to a web of subjects, institutions, texts, and authors that Before and after those publications, CDS has covered tend to remain invisible to researchers who prefer to a wide area of communications inquiry, including data treat Big Data science as a new form of positivism—but power issues in social media, apps, the Internet, web, the ‘‘data’’ of ‘‘Big Data’’ are not always the whole and platforms, but also and equally importantly statis- story. As Foucault famously put it in The Archaeology tics, policy, research, and organization. In every way of Knowledge, the figures that populate a field do not that data are organized in a communicative context, communicate only by the logical successions of prop- CDS—as a clear call for the critical investigation of ositions but also by the ‘‘positivity of their discourse’’ Big Data science—has coalesced around researchers which defines a field where ‘‘formal identities, thematic ready to deploy pronounced critical frameworks in continuities, translations of concepts, and polemical order to foreground data’s power structures. Of interchanges may be deployed’’ (2002: 143). Similarly, course, such a field understandably runs the risk of Big Data must be challenged by acknowledging the being overly broad and presumptuously inclusive. limitations of the positivity of its discourse and the As Dalton et al. (2016) note, CDS might offend realities of the shifting information infrastructures researchers who point out that all forms of research (Bowker et al., 2010), multiple data subjects and their are critical and create a false separation between critical rights (Haraway, 1991; Jones, 2016), deep information theory and data science. As such, CDS continues to histories (Beniger, 1989), work and power (Zuboff, remain an inclusive field that is open to self-critique 1988), and hybrid digital cultures (Striphas, 2016) that and dialog, itself politicized in its quest to politicize underpin it. Big Data. At the very least, the amorphous groups of individuals, texts, projects, and institutions that seek a specific and pronounced critical engagement with Big Critical data studies (CDS) Data science now have a name to use. One way that a critical approach to Big Data contrib- The multidimensionality of possible critiques of Big utes to knowledge is by helping define the questions Data science grows out of the plurality of data them- that inform epistemological frameworks around social selves. In their ability to provide interpretations of real- ity, data are apprehended through various levels of issues related to data. A critical approach to Big Data investigates meta-theoretical modes of conversation informational abstraction (Floridi, 2011) that frame and styles of scientific thinking (Hacking, 1994) that what data are about. Such frameworks and perspectives pervade data science—it does not contribute to know- on Big Data are multiple and diverse and may attend ledge in the positivistic sense but instead analyzes the to any number of apparatuses that reflect specific sub- ground upon which positivistic Big Data science stands. ject positions. Levels of informational abstraction—the Iliadis and Russo 3 product of positionalities that constrain and afford mainly to impede science-based policy processes what data can be about—are a gateway into the mul- weaponize the concept of data transparency’’ (1). tiple roles that data play and the ways that abstraction Similarly, work in CDS should be attuned to the differ- may be adopted, manipulated, or repurposed for any ent ways that data can be hijacked and/or weaponized number of aims. Choosing a level of abstraction from to substantiate pseudoscientific claims that belie polit- which to view Big Data alters the types of conversations ical motivations. that can be had about data, its aims, and functions. Bronson and Knezevic (2016) take up the issue of As noted by Kitchin (2014) and Kitchin and Big Data in food and agriculture. They review data Lauriault (2014), the subjects of CDS are the sociotech- applications in the agricultural food sector and note nical ‘‘data assemblages’’ that make up Big Data. The that such analytics tools have ‘‘implications for rela- apparatus and elements of a data assemblage may tionships of power between players in the food system include systems of thought, forms of knowledge, (e.g., between farmers and large corporations)’’ (1), finance, political economy, governmentalities and leg- looking at issues such as data ownership in the context alities, materialities and infrastructures, practices, of applications like Monsanto’s Weed ID app, as well organizations and institutions, subjectivities and com- as the privacy implications of John Deere’s precision munities, places, and the marketplace where data are agricultural equipment. CDS works in food and agri- constituted. Assemblages should be understood as culture platforms tend to be less visible compared to structures that emerge as constitutive of Big Data, their more traditional social media counterparts. viewed from a variety of social positions at multiple As such, CDS calls for the critical investigation of scales (local, national, international) that exert power. data-intensive fields that exist outside the ken of trad- Data assemblages are the powerful complex of entities itional ‘‘media theory’’ literature, such as food and that form the underlying production of Big Data sci- agriculture processing data. While traditional social ence at multiple levels of abstraction and in a plurality media platforms such as Facebook and Twitter must of domains. remain open to CDS work, CDS must further attend to critical data problems in multiple data science domains, from data science’s use in food and agricul- CDS work ture to Big Data techniques in environmental and So far, CDS has emerged as a loose knit group of financial regulation. frameworks, proposals, questions, and mani- Dalton et al. (2016) offer a welcome and open dialog festos—something to be expected of fields still in their on data, time, and space. Their contribution takes the infancy. What need to be established are long-term pro- form of a three-way interview (a valuable format that jects that take up specific challenges in CDS by propos- should be used more often). CDS originated in the field ing critical investigations into Big Data assemblages. In of geography and this article builds on Dalton and this Big Data & Society CDS special theme, we collect a Thatcher’s (2014) original call for CDS work that group of articles that seek to build on the earlier work focuses on problems deeply connected to locality and of CDS researchers to expose Big Data science prob- identity. Issues related to the Big Data divide, data lems in social contexts. These articles and commen- discourses, data subjects, and data corporations are taries deal with a variety of themes and issues related framed as central to CDS. Dalton et al. discuss ‘‘the to Big Data science, ranging from food and health to stakes, ideas, responsibilities, and possibilities of critical policing and the environment. The articles feature data studies’’ (1) and in doing so continue the practice many issues and subjects that have been of concern to of open, sensitive, and politicized dialog among CDS CDS researchers and offer a glimpse into the types of researchers. As a plural and multifaceted field of scholarship that CDS work might continue to produce inquiry, CDS should continue to be open to such with renewed attention. forms of dialog, self-critique, and coinvestigation. For example, Levy and Johns (2016) note that Big Moving from self-critique to a critical engagement of Data can be counterintuitively ‘‘weaponized’’ under the governmental data practices, Rieder and Simon (2016) veil of openness and transparency and responsible data discuss data’s influence on truth and objectivity in the practices. While Levy and Johns generally agree with science of governance. They highlight a growing inter- data safety practices, they argue that ‘‘legislative efforts est in evidenced-based policy-making and provide an account of data-driven forms of governance. Should that invoke the language of data transparency can sometimes function as ‘Trojan Horses’ through which numerical evidence produced by Big Data science other political goals are pursued’’ (1). Through an serve as mandate for the production of policy and investigation of the ‘‘sound science’’ initiatives of the new forms of governance? Such questions are beginning 1990s and current efforts to open environmental data to to be addressed among CDS researchers who look to public inspection, they find that ‘‘[r]ules that exist interrogate the ways Big Data are used to support 4 Big Data & Society changes in governmentality and social organization, as surrounding the use of Big Data in humanitarian well as issues related to social policy and practices. contexts. Another policy issue that should be of interest to Beyond humanitarian social data problems, CDS scholars is the use of human subjects in research. sociotechnical systems that populate the worlds of eco- Metcalf and Crawford (2016) address the fundamen- nomics, finance, and the stock market pose a signifi- tally important question of ‘‘Where are human subjects cant challenge to CDS due to their closed, inaccessible in Big Data research?’’ In discussing the emerging nature. Further, semiautomated systems like the stock ethics divide, Metcalf and Crawford chart what they market and high frequency trading pose new questions view as the ‘‘growing discontinuities between the in terms of data subjects and subjectivity. Christiaens research practices of data science and established (2016) provides a critical inquiry into digital subjecti- tools of research ethics regulation’’ (1). Making the vation in the world of finance, writing that ‘‘traders claim that certain features of ethics regulations have been steadily integrated into computerized data cannot be adequately transferred from biomedical assemblages, which calls for an ontology that elimin- research to data science research, Metcalf and ates the distinction between human sovereign subjects Crawford find that this has led some data science and non-human instrumental objects’’ (1). Building on researchers to eschew ethical considerations relating the work of Maurizio Lazzarato, Christiaens provides to data subjects. Their article discusses current debates a critical take on human–machine interaction, arguing around the USA’s Common Rule regarding the regu- that the high-speed data-driven nature of financial lation of human subjects research and investigates the markets subjectivize traders in preconscious ways due regulation of social science research, arguing that ‘‘data to their inability to keep apace with automated trans- science should be understood as continuous with social actions. Christiaens argues that CDS must consider sciences in this regard’’ (1). In emphasizing the ethical processes of digital subjectivation and subjugation dimensions of public datasets and their subjects, that occur when Big Data science is applied to socio- Metcalf and Crawford call attention to a growing prob- technical systems that are governed by humans and lem in Big Data science. machines. Moving from data subjects to data places, Perng The theme of subjectivity is raised throughout these et al. (2016) take up the question of locative media papers in part due to the lack of discussion around human subjects in Big Data research. Perhaps the and data-driven computing experiments. They note the various ways in which ‘‘exploratory data-driven most vulnerable, minority and lower socioeconomic computing experiments’’ that use geocoding ‘‘seek to status subjects are affected by Big Data science in find ways to extract value and insight’’ (1) and raise often invisible and unforeseen ways. Currie et al. the concern that such practices often begin from data (2016) provide an example of such a case in their ana- rather than from theory. They argue that locative lysis of four datasets containing police officer-involved media data and computing experiments attempt to homicide statistics in Los Angeles. Their paper frames derive possible futures while having unintended conse- ‘‘police officer-involved homicide data as a rhetorical quences. They further argue that ‘‘using computing tool that can reify certain assumptions about the experiments to imagine potential urban futures pro- world and extend regimes of power’’ (1). Civic data, duces effects that often have little to do with creating they argue, can be incorporated into creative commu- new urban practices’’ (1). Rather, Perng et al. note that nity practice and events as a form of datactivism. such experiments serve to promote Big Data science Comparing local, regional, and national datasets on and the notion that data may be repurposed. police officer-involved homicides in Los Angeles, the Tackling another side of Big Data science and its authors provide ‘‘accounts of the semantics, granular- relationship to different localities, Mulder et al. (2016) ity, scale and transparency’’ of the data before des- look into the growing issue of crowdsourced crisis data cribing a ‘‘counter data action’’ (1) event held with and humanitarian work. Their aim is to investigate community members. whether Big Data can contribute to an inclusive Whether subjects can trust Big Data is a reoccurring humanitarian response during large crises. They argue concern and Symons and Alvarado (2016) take up this that Big Data are ‘‘socially constructed artefacts that question. Applying philosophy of science to software, reflect the contexts and processes of their creation’’ (1) Symons and Alvarado address some of the epistemo- across local and international contexts and analyze Big logical challenges posed by Big Data while addressing Data-making processes in the context of the 2010 Haiti the topics of computational modeling and simulation. and 2015 Nepal earthquakes. They find that ‘‘locally The authors take up the issue of ‘‘epistemic opacity’’ based, affected people [...] are marginalized in their while investigating the problem of error management ability to benefit from Big Data in support of their and error detection. Paying special attention to the own means’’ (1). As such, their work adds to debates relationship between error and path complexity in Iliadis and Russo 5 software, the article provides an overview of statistical derivative nature of online metadata in terms of meta- methods and reviews their limitations. data’s ability to potentially identify human users. The Finally, the CDS special theme concludes with two identification of social data problems should pair articles that critically examine the use of data derived Big Data science with common problems, allowing from computational modeling for epidemiology and the researchers to consider the shared nature of a problem- study of environmental pollution. Canali (2016) atic and to formulate ways in which it might be com- addresses the complicated issue of data-driven science’s monly articulated. This is not a transparent process and limitations and connection to causality. He focuses researchers should give ample thought to articulating on Big Data and causal knowledge by examining problematic scenarios involving social data. Critical EXPOsOMICS, a European Commission-funded pro- framework designs include viewing data as interpretive ject aiming to improve understanding of the relation and rhetorical assemblages in the construction of between exposure and disease. Canali shows how science, institutions, and citizens. Established critical causal knowledge is necessary for EXPOsOMICS and frameworks in CDS such as those oriented around argues that ‘‘data-driven claims about causality are data assemblages are just some of the possible direc- fundamentally flawed’’ (1), suggesting that causal tions for CDS frameworks, though it should not be knowledge must remain a necessary part of Big Data forgotten that CDS also consist of forms of datactivism science. Thoreau (2016) examines the use of computa- and should contribute to data literacy and data justice. tional models and their data to determine environmen- The application of social solutions to increase data lit- tal toxicity and assist in the regulation of chemicals. eracy and justice involves effecting change by conduct- They conclude that quantitative structure-activity ing research and sharing that research and the activities relationship models as causal explanation should be that might grow out of it with the public. Importantly, reconsidered by regulators. CDS should provide individuals with the necessary tools for becoming more informed and the ability to organize efforts around data justice issues. By main- Orientations and principles taining these orientations and principles, CDS should Each of the articles in this Big Data & Society special encourage us to think about Big Data science in terms theme share core concerns that we view as important to of the common good and social contexts. CDS. We will end by summarizing three general orien- tations and principles. Declaration of conflicting interests In his Nicomachean Ethics, Aristotle famously refers The author(s) declared no potential conflicts of interest with to an ‘‘education for the common good’’—a perspective respect to the research, authorship, and/or publication of this that can nurture care by encouraging a shared under- article. standing of specialized knowledge while emphasizing the importance of collective learning and interaction. Funding The notion of education for the common good deeply The author(s) received no financial support for the research, informs CDS frameworks which should be built to inte- authorship, and/or publication of this article. grate participatory learning and research. In our view, CDS follows three basic principles derived from this broadly Aristotelean approach: the identification of Notes social data problems, the design of critical frameworks 1. This Big Data & Society special theme on CDSs grew out for addressing social data problems, and the applica- of the Society for the Philosophy of Information’s Seventh tion of social solutions to increase data literacy. These Workshop, ‘‘Conceptual Challenges of Data in Science three simple principles allow for a collective learning and Technology’’ (2015, University College London). experience where critical approaches can be put to use http://www.socphilinfo.org/ in specific contexts. 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Journal

Big Data & SocietySAGE

Published: Oct 17, 2016

Keywords: Critical Data Studies; Big Data; data science; data ethics; data subjects

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