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Big Data & Society
Number of Followers: 40  Open Access journalISSN (Online) 2053-9517 Published by Sage Publications  [1176 journals]
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- Surveillance capitalism and systemic digital risk: The imperative to
collect and connect and the risks of interconnectedness
Authors: Dean Curran
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Zuboff's The Age of Surveillance Capitalism provides a powerful analysis of the emergence of surveillance capitalism as a particular type of informational capitalism. Many of the important impacts of this project of creating larger and more integrated systems of ‘behavioural surplus’ are captured powerfully by Zuboff; yet as different risk and organisational scholars such as Beck, Perrow, and Vaughan have argued, integrated systems often do not function as intended. While the imperfection of these systems may raise the possibility that surveillance capitalism may not be as bad as Zuboff suggests, there is also a way in which these systems not functioning as intended can make surveillance capitalism an even more dystopian possibility. In this vein, this paper asks: what are the consequences when the tools of a surveillance capitalist society break down' This paper argues that it is by thinking through Zuboff's framework that we can identify the systemic fragility of a surveillance capitalist society. This systemic fragility emerges through how surveillance capitalism generates imperatives towards the maximal collection of data for exploitation, which in turn generates a corresponding imperative to connect all aspects of life. Both of these imperatives, of collect and connect, in turn create an immensely fragile digital system, which has vast ramifications throughout social life, such that small imperfections and gaps in the system can magnify risk throughout society.
Citation: Big Data & Society
PubDate: 2023-05-25T05:16:19Z
DOI: 10.1177/20539517231177621
Issue No: Vol. 10, No. 1 (2023)
- The politics of the NPC meme: Reactionary subcultural practice and
vernacular theory
Authors: Rob Gallagher, Robert Topinka
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The acronym ‘NPC’ originates from videogame culture, where it refers to computer-controlled drones whose behaviour is dictated by their programming. By 2018 the term had gained traction within right-wing subcultural spaces as shorthand for individuals apparently incapable of thinking for themselves. By the autumn of 2018, these spaces were awash with NPC memes accusing liberals and leftists of uncritically accepting progressive doxa and parroting left-wing catchphrases. In mid-October, with midterm elections looming in the US, Twitter banned over 1000 NPC roleplay accounts created by supporters of Donald Trump, citing concerns over disinformation. This event was much discussed both within right-wing subcultural spaces and by mainstream media outlets, serving as an occasion to reassess the political effects of digital media in general and reactionary memes in particular. Here we use a combination of computational analysis and theoretically informed close reading to trace the NPC meme's trajectory and explore its role in entrenching affectively charged political and (sub)cultural faultlines. We show how mainstream attention at once amplified the meme and attenuated its affective resonance in the subcultural spaces where it originated. We also contend that while the NPC meme has served as a vehicle for antidemocratic bigotry, it may yet harbour critical potential, providing a vocabulary for theorising the cultural and political impacts of communicative capitalism.
Citation: Big Data & Society
PubDate: 2023-05-16T10:50:05Z
DOI: 10.1177/20539517231172422
Issue No: Vol. 10, No. 1 (2023)
- ‘I’ve left enough data’: Relations between people and data and the
production of surveillance
Authors: Hwankyung Janet Lee
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Exploring emergent relations between data-producing individuals and their data products, this study aims to contribute to the ongoing scholarly discussion on agencies in data practices. It focuses on shifts in surveillance structure in the era of Big Data, in which the individual becomes both a subject and an object in the production of data surveillance. Drawing on the concept of the ‘dividual’, the study analyses data practices for a tracing system invented by the South Korean government during the COVID-19 pandemic, with findings from field research conducted with 11 research participants in various urban sites in Seoul. Highlighting how the tracing system positioned surveillance ‘in the hands of citizens’, the study exposes the complexities of the relations that the participants formed with the data they produced, and how they reflexively reappropriated their practices through alterations and deflections on the basis of their tacit knowledge and imaginaries concerning digital data and their constituent positions in the knowledge production system. The resultant expression of surveillance was directly shaped by the evolving relationship between the producers (participants) and products (digital data). The study proposes that an intersectional focus on surveillance and critical data studies, with close attention to ordinary people's relations with data, has the capacity to inquire into the politics of data more fully.
Citation: Big Data & Society
PubDate: 2023-05-10T05:53:12Z
DOI: 10.1177/20539517231173904
Issue No: Vol. 10, No. 1 (2023)
- Stepping back from Data and AI for Good – current trends and ways
forward
Authors: Ville Aula, James Bowles
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Various ‘Data for Good’ and ‘AI for Good’ initiatives have emerged in recent years to promote and organise efforts to use new computational techniques to solve societal problems. The initiatives exercise ongoing influence on how the capabilities of computational techniques are understood as vehicles of social and political change. This paper analyses the development of the initiatives from a rhetorical slogan into a research program that understands itself as a ‘field’ of applications. It discusses recent academic literature on the topic to show a problematic entanglement between the promotion of initiatives and prescriptions of what ‘good’ ought to be. In contrast, we call researchers to take a practical and analytical step back. The paper provides a framework for future research by calling for descriptive research on the composition of the initiatives and critical research that draws from broader social science debates on computational techniques. The empirical part of the paper provides first steps towards this direction by positioning Data and AI for Good initiatives as part of a single continuum and situating it within a historical trajectory that has its immediate precursor in ICT for Development initiatives.
Citation: Big Data & Society
PubDate: 2023-05-10T05:52:23Z
DOI: 10.1177/20539517231173901
Issue No: Vol. 10, No. 1 (2023)
- Modeling COVID-19 with big mobility data: Surveillance and reaffirming the
people in the data
Authors: Thomas Walsh
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
To better understand the COVID-19 pandemic, public health researchers turned to “big mobility data”—location data collected from mobile devices by companies engaged in surveillance capitalism. Publishing formerly private big mobility datasets, firms trumpeted their efforts to “fight” COVID-19 and researchers highlighted the potential of big mobility data to improve infectious disease models tracking the pandemic. However, these collaborations are defined by asymmetries in information, access, and power. The release of data is characterized by a lack of obligation on the part of the data provider towards public health goals, particularly those committed to a community-based, participatory model. There is a lack of appropriate reciprocities between data company, data subject, researcher, and community. People are de-centered, surveillance is de-linked from action while the agendas of public health and surveillance capitalism grow closer. This article argues that the current use of big mobility data in the COVID-19 pandemic represents a poor approach with respect to community and person-centered frameworks.
Citation: Big Data & Society
PubDate: 2023-05-10T04:46:00Z
DOI: 10.1177/20539517231164115
Issue No: Vol. 10, No. 1 (2023)
- Ethical scaling for content moderation: Extreme speech and the
(in)significance of artificial intelligence
Authors: Sahana Udupa, Antonis Maronikolakis, Axel Wisiorek
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
In this article, we present new empirical evidence to demonstrate the severe limitations of existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. Building on the collaborative coding project “AI4Digntiy,” we outline the ambiguities and complexities of annotating problematic text in AI-assisted moderation systems. We diagnose the shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of “the human.” Presenting a decolonial critique of the “human vs machine” conundrum and drawing attention to the structuring effects of coloniality on extreme speech, we propose “ethical scaling” to highlight moderation process as political praxis. As a normative framework for platform governance, ethical scaling calls for a transparent, reflexive, and replicable process of iteration for content moderation with community participation and global parity, which should evolve in conjunction with addressing algorithmic amplification of divisive content and resource allocation for content moderation.
Citation: Big Data & Society
PubDate: 2023-05-09T05:11:19Z
DOI: 10.1177/20539517231172424
Issue No: Vol. 10, No. 1 (2023)
- Prediction as extraction of discretion
Authors: Sun-ha Hong
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
I argue that prediction is not primarily a technological means for knowing future outcomes, but a social model for extracting and concentrating discretionary power. Prediction is a ‘relational grammar’ that governs this allocation of discretion: the everyday ability to define one's situation. This extractive dynamic extends a long historical pattern, in which new methods for producing knowledge entail a redistribution of decision-making power. I focus on two contemporary domains: (1) crime and policing are emblematic of how predictive systems are extractive by design, with pre-existing interests governing what is measured and what persistently goes unmeasured. (2) The prediction of productivity demonstrates the long tradition of extracting discretion as a means to extract labour power. Time after time, making human behaviour more predictable for the client of prediction (the manager, the police officer) often means making life and work more unpredictable for the target of prediction (the employee, the urban citizen).
Citation: Big Data & Society
PubDate: 2023-05-09T05:10:10Z
DOI: 10.1177/20539517231171053
Issue No: Vol. 10, No. 1 (2023)
- FAIR data sharing: An international perspective on why medical researchers
are lagging behind
Authors: Linda Rainey, Jennifer E Lutomski, Mireille JM Broeders
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.
Citation: Big Data & Society
PubDate: 2023-05-05T05:34:53Z
DOI: 10.1177/20539517231171052
Issue No: Vol. 10, No. 1 (2023)
- Deleterious consequences: How Google's original sociotechnical affordances
ultimately shaped ‘trusted users’ in surveillance capitalism
Authors: Renée Ridgway
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Google dominates around 92% of the search market worldwide (as of November 2022), with most of its revenue derived from search advertising. However, Google's hegemony over search and the resulting implications are not necessarily accidental, arbitrary or (un)intentional. This article revisits Brin and Page's original paper, drawing on six of their key innovations, concerns and design choices (counting citations or backlinks, trusted user, advertising, personalization, usage data, smart algorithms) to explain the evolution of Google's hypertext search engine technologies through ‘moments of contingency’, which led to corporate lock-ins. Underpinned by analyses of patents, statements and secondary sources, it elucidates how early Google considerations and certain affordances not only came to shape the web (backlinks, trusted user, advertising) but subsequently facilitated contemporary surveillance capitalism. Building upon Zuboff's ‘Big Other’, it describes the ways in which Google as an infrastructure is intertwined with Big Data's platformization and the ad infinitum collection of usage data, beyond just personalization. This extraction and refinement of usage data as ‘behavioural surplus’ results in ‘deleterious consequences’: a ‘habit of automaticity,’ which shapes the trusted user through ‘ubiquitous googling’ and smart algorithms, whilst simultaneously generating prediction products for surveillance capitalism. Advancing Latour's ‘predicting the path’ of technological innovation, this cause-and-effect story contributes a new taxonomy of Google sociotechnical affordances to critical STS, media history and web search literature.
Citation: Big Data & Society
PubDate: 2023-05-02T05:52:52Z
DOI: 10.1177/20539517231171058
Issue No: Vol. 10, No. 1 (2023)
- Big ideas, small data: Opportunities and challenges for data science and
the social services sector
Authors: Geri Louise Dimas, Lauri Goldkind, Renata Konrad
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The social services sector, comprised of a constellation of programs meeting critical human needs, lacks the resources and infrastructure to implement data science tools. As the use of data science continues to expand, it has been accompanied by a rise in interest and commitment to using these tools for social good. This commentary examines overlooked, and under-researched limitations of data science applications in the social sector—the volume, quality, and context of the available data that currently exists in social service systems require unique considerations. We explore how the presence of small data within the social service contexts can result in extrapolation; if not properly considered, data science can negatively impact the organizations data scientists are trying to assist. We conclude by proposing three ways data scientists interested in working within the social services sector can enhance their contributions to the field: refining and leveraging available data, improving collaborations, and respecting data limitations.
Citation: Big Data & Society
PubDate: 2023-05-02T05:51:53Z
DOI: 10.1177/20539517231171051
Issue No: Vol. 10, No. 1 (2023)
- Extrapolation and AI transparency: Why machine learning models should
reveal when they make decisions beyond their training
Authors: Xuenan Cao, Roozbeh Yousefzadeh
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The right to artificial intelligence (AI) explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which can reveal whether a model is making inferences beyond the boundaries of its training. We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency, accountability, and fairness. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our commentary accompanies practical clauses useful to include in AI regulations such as the AI Bill of Rights, the National AI Initiative Act in the United States, and the AI Act by the European Commission.
Citation: Big Data & Society
PubDate: 2023-04-21T09:45:04Z
DOI: 10.1177/20539517231169731
Issue No: Vol. 10, No. 1 (2023)
- Organic online politics: Farmers, Facebook, and Myanmar's military coup
Authors: Hilary Oliva Faxon, Kendra Kintzi, Van Tran, Kay Zak Wine, Swan Ye Htut
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Despite perennial hope in the democratic possibilities of the internet, the rise of digital authoritarianism threatens online and offline freedom across much of the world. Yet while critical data studies has expanded its geographic focus, limited work to date has examined digital mobilization in the agrarian communities that comprise much of the Global South. This article advances the concept of “organic online politics,” to demonstrate how digital mobilization grows from specific rural conditions, material concerns, and repertoires of resistance, within the constraints of authoritarian violence and internet control. To do so, we examine social media interaction in the wake of the 2021 military coup in Myanmar, an agrarian nation with recent, rapid digital connection that corresponded with a decade-long democratic turn. Analyzing an original archive of over 2000 Facebook posts collected from popular farming pages and groups, we find a massive drop-off in online activity after the military coup and analyze the shifting temporalities of digital mobilization. Crucially, we highlight the embeddedness of online interaction within the material concerns of farming communities, examining how social media become a key forum for negotiating political crisis in Myanmar's countryside. These findings call attention to rural digital subcultures as fertile sites of investigation and point toward the need for future scholarship on data practices that attends to rooted agrarian struggles.
Citation: Big Data & Society
PubDate: 2023-04-17T06:16:23Z
DOI: 10.1177/20539517231168101
Issue No: Vol. 10, No. 1 (2023)
- Predictive privacy: Collective data protection in the context of
artificial intelligence and big data
Authors: Rainer Mühlhoff
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Big data and artificial intelligence pose a new challenge for data protection as these techniques allow predictions to be made about third parties based on the anonymous data of many people. Examples of predicted information include purchasing power, gender, age, health, sexual orientation, ethnicity, etc. The basis for such applications of “predictive analytics” is the comparison between behavioral data (e.g. usage, tracking, or activity data) of the individual in question and the potentially anonymously processed data of many others using machine learning models or simpler statistical methods. The article starts by noting that predictive analytics has a significant potential to be abused, which manifests itself in the form of social inequality, discrimination, and exclusion. These potentials are not regulated by current data protection law in the EU; indeed, the use of anonymized mass data takes place in a largely unregulated space. Under the term “predictive privacy,” a data protection approach is presented that counters the risks of abuse of predictive analytics. A person's predictive privacy is violated when personal information about them is predicted without their knowledge and against their will based on the data of many other people. Predictive privacy is then formulated as a protected good and improvements to data protection with regard to the regulation of predictive analytics are proposed. Finally, the article points out that the goal of data protection in the context of predictive analytics is the regulation of “prediction power,” which is a new manifestation of informational power asymmetry between platform companies and society.
Citation: Big Data & Society
PubDate: 2023-04-17T06:14:43Z
DOI: 10.1177/20539517231166886
Issue No: Vol. 10, No. 1 (2023)
- Recording the ethical provenance of data and automating data stewardship
Authors: Alexander Bernier, Maili Raven-Adams, Davide Zaccagnini, Bartha M. Knoppers
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Health organisations use numerous different mechanisms to collect biomedical data, to determine the applicable ethical, legal and institutional conditions of use, and to reutilise the data in accordance with the relevant rules. These methods and mechanisms differ from one organisation to another, and involve considerable specialised human labour, including record-keeping functions and decision-making committees. In reutilising data at scale, however, organisations struggle to meet demands for data interoperability and for rapid inter-organisational data exchange due to reliance on legacy paper-based records and on the human-initiated administration of accompanying permissions in data. The adoption of permissions-recording, and permissions-administration tools that can be implemented at scale across numerous organisations is imperative. Further, these must be implemented in a manner that does not compromise the nuanced and contextual adjudicative processes of research ethics committees, data access committees, and biomedical research organisations. The tools required to implement a streamlined system of biomedical data exchange have in great part been developed. Indeed, there remains but a small core of functions that must further be standardised and automated to enable the recording and administration of permissions in biomedical research data with minimal human effort. Recording ethical provenance in this manner would enable biomedical data exchange to be performed at scale, in full respect of the ethical, legal, and institutional rules applicable to different datasets. This despite foundational differences between the distinct legal and normative frameworks is applicable to distinct communities and organisations that share data between one another.
Citation: Big Data & Society
PubDate: 2023-04-17T06:14:05Z
DOI: 10.1177/20539517231163174
Issue No: Vol. 10, No. 1 (2023)
- The importance of algorithm skills for informed Internet use
Authors: Jonathan Gruber, Eszter Hargittai
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Using the Internet means encountering algorithmic processes that influence what information a user sees or hears. Existing research has shown that people's algorithm skills vary considerably, that they develop individual theories to explain these processes, and that their online behavior can reflect these understandings. Yet, there is little research on how algorithm skills enable people to use algorithms to their own benefit and to avoid harms they may elicit. To fill this gap in the literature, we explore the extent to which people understand how the online systems and services they use may be influenced by personal data that algorithms know about them, and whether users change their behavior based on this understanding. Analyzing 83 in-depth interviews from five countries about people's experiences with researching and searching for products and services online, we show how being aware of personal data collection helps people understand algorithmic processes. However, this does not necessarily enable users to influence algorithmic output, because currently, options that help users control the level of customization they encounter online are limited. Besides the empirical contributions, we discuss research design implications based on the diversity of the sample and our findings for studying algorithm skills.
Citation: Big Data & Society
PubDate: 2023-04-13T04:17:32Z
DOI: 10.1177/20539517231168100
Issue No: Vol. 10, No. 1 (2023)
- Machine learning, meaning making: On reading computer science texts
Authors: Louise Amoore, Alexander Campolo, Benjamin Jacobsen, Ludovico Rella
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.
Citation: Big Data & Society
PubDate: 2023-03-31T05:10:06Z
DOI: 10.1177/20539517231166887
Issue No: Vol. 10, No. 1 (2023)
- “Too Soon” to count' How gender and race cloud notability
considerations on Wikipedia
Authors: Mackenzie Emily Lemieux, Rebecca Zhang, Francesca Tripodi
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
While research has explored the extent of gender bias and the barriers to women's inclusion on English-language Wikipedia, very little research has focused on the problem of racial bias within the encyclopedia. Despite advocacy groups' efforts to incrementally improve representation on Wikipedia, much is unknown regarding how biographies are assessed after creation. Applying a combination of web-scraping, deep learning, natural language processing, and qualitative analysis to pages of academics nominated for deletion on Wikipedia, we demonstrate how Wikipedia's notability guidelines are unequally applied across race and gender. We find that online presence predicts whether a Wikipedia page is kept or deleted for white male academics but that this metric is idiosyncratically applied for female and BIPOC academics. Further, women's pages, regardless of race, were more likely to be deemed “too soon” for Wikipedia. A deeper analysis of the deletion archives reveals that when the tag is used on a woman's biography it is done so outside of the community guidelines, referring to one's career stage rather than media/online coverage. We argue that awareness of hidden biases on Wikipedia is critical to the objective and equitable application of the notability criteria across race and gender both on the encyclopedia and beyond.
Citation: Big Data & Society
PubDate: 2023-03-30T04:27:52Z
DOI: 10.1177/20539517231165490
Issue No: Vol. 10, No. 1 (2023)
- Coloniality and frictions: Data-driven humanitarianism in North-Eastern
Nigeria and South Sudan
Authors: Vicki Squire, Modesta Alozie
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
It is now over a decade since the proclamation of a humanitarian ‘data revolution’, with the rise of ‘innovation’ and the proliferation of ‘data solutions’ rendering data-based humanitarianism an important area of critical investigation. This article contributes to debates within the field by exploring the role of data in the provision of humanitarian assistance within camps for internally displaced persons (IDPs) across north-eastern Nigeria and South Sudan. It draws on qualitative interviews carried out with humanitarian practitioners specialising in data and information management, as well as with camp residents and stakeholders located in each region. The analysis focuses attention on the ways in which epistemic injustices have been further perpetuated by the ‘data revolution’ due to the intensification of paternalistic dynamics associated with the coloniality of humanitarianism. It shows how a logic of extractivism structures the humanitarian data ecosystem, while also generating a series of tensions and disagreements. Data-driven humanitarianism, the article concludes, is characterised by recurring colonial dynamics as well as intensified frictions that bring epistemic injustices into sharper focus.
Citation: Big Data & Society
PubDate: 2023-03-30T03:42:22Z
DOI: 10.1177/20539517231163171
Issue No: Vol. 10, No. 1 (2023)
- When research is the context: Cross-platform user expectations for social
media data reuse
Authors: Sarah Gilbert, Katie Shilton, Jessica Vitak
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Social media provides unique opportunities for researchers to learn about a variety of phenomena—it is often publicly available, highly accessible, and affords more naturalistic observation. However, as research using social media data has increased, so too has public scrutiny, highlighting the need to develop ethical approaches to social media data use. Prior work in this area has explored users’ perceptions of researchers’ use of social media data in the context of a single platform. In this paper, we expand on that work, exploring how platforms and their affordances impact how users feel about social media data reuse. We present results from three factorial vignette surveys, each focusing on a different platform—dating apps, Instagram, and Reddit—to assess users’ comfort with research data use scenarios across a variety of contexts. Although our results highlight different expectations between platforms depending on the research domain, purpose of research, and content collected, we find that the factor with the greatest impact across all platforms is consent—a finding which presents challenges for big data researchers. We conclude by offering a sociotechnical approach to ethical decision-making. This approach provides recommendations on how researchers can interpret and respond to platform norms and affordances to predict potential data use sensitivities. The approach also recommends that researchers respond to the predominant expectation of notification and consent for research participation by bolstering awareness of data collection on digital platforms.
Citation: Big Data & Society
PubDate: 2023-03-28T07:13:49Z
DOI: 10.1177/20539517231164108
Issue No: Vol. 10, No. 1 (2023)
- ‘I started seeing shadows everywhere’: The diverse chilling effects of
surveillance in Zimbabwe
Authors: Amy Stevens, Pete Fussey, Daragh Murray, Kuda Hove, Otto Sake
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Recent years have witnessed growing ubiquity and potency of state surveillance measures with heightened implications for human rights and social justice. While impacts of surveillance are routinely framed through ‘privacy’ narratives, emphasising ‘chilling effects’ surfaces a more complex range of harms and rights implications for those who are, or believe they are, subjected to surveillance. Although first emphasised during the McCarthy era, surveillance ‘chilling effects’ remain under-researched, particularly in Africa. Drawing on rare interview data from participants subjected to state-sponsored surveillance in Zimbabwe, the paper reveals complex assemblages of state and non-state actors involved in diverse and expansive hybrid online–offline monitoring. While scholarship has recently emphasised the importance of large-scale digital mass surveillance, the Zimbabwean context reveals complex assemblages of ‘big data’, social media and other digital monitoring combining with more traditional human surveillance practices. Such inseparable online–offline imbrications compound the scale, scope and impact of surveillance and invite analyses as an integrated ensemble. The paper evidences how these surveillance activities exert chilling effects that vary in form, scope and intensity, and implicate rights essential to the development of personal identity and effective functioning of participatory democracy. Moreover, the data reveals impacts beyond the individual to the vicarious and collective. These include gendered dimensions, eroded interpersonal trust and the depleted ability of human rights defenders to organise and particulate in democratic processes. Overall, surveillance chilling effects exert a wide spectrum of outcomes which consequently interfere with enjoyment of multiple rights and hold both short- and long-term implications for democratic participation.
Citation: Big Data & Society
PubDate: 2023-03-22T06:13:31Z
DOI: 10.1177/20539517231158631
Issue No: Vol. 10, No. 1 (2023)
- Smart corruption: Satirical strategies for gaming accountability
Authors: Ritwick Ghosh, Hilary Oliva Faxon
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Although new forms of data can be used to hold power to account, they also grant the powerful new resources to game accountability. We dub the latter behavior “smart corruption.” The concept highlights the possibility of appropriating algorithms, infrastructures, and data publics to accumulate benefits and obscure responsibility while leaning into the positive associations of transparency. Unlike conventional forms of corruption, smart corruption is disguised as progressive, and is thus difficult to spot or analyze through existing legal or ethical frameworks. To illustrate, we outline a satirical strategy for gaming accountability. Identifying the particular mechanisms and outcomes of transgressive activities carried out under the veneer of data-driven transparency, as well as the key actors and organizations most active in gaming accountability, is an important research and political project.
Citation: Big Data & Society
PubDate: 2023-03-21T05:37:33Z
DOI: 10.1177/20539517231164119
Issue No: Vol. 10, No. 1 (2023)
- Not so fast! Data temporalities in law enforcement and border control
Authors: Matthias Leese, Silvan Pollozek
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
In this paper, we investigate the temporal implications of data in law enforcement and border control. We start from the assumption that the velocity of knowledge and action is defined by heterogeneous formations and interactions of various actors, sites, and materials. To analyze these formations and interactions, we introduce and unpack the concept of “data temporality.” Data temporality explicates how the speed of knowledge and action in datafied environments unfolds in close correspondence with (1) variegated social rhythms, (2) technological inscriptions, and (3) the balancing of speed with other priorities. Specifically, we use the notion of data temporality as a heuristic tool to explore the entanglements of data and time within two case studies: Frontex’ Joint Operation Reporting Application and the predictive policing software PRECOBS. The analysis identifies two key themes in the empirical constitution of data temporalities. The first one pertains to the creation of events as reference points for temporally situated knowledge and action. And the second one pertains to timing and actionability, that is, the question of when interventions based on data analysis should be triggered.
Citation: Big Data & Society
PubDate: 2023-03-20T02:22:20Z
DOI: 10.1177/20539517231164120
Issue No: Vol. 10, No. 1 (2023)
- Fact signalling and fact nostalgia in the data-driven society
Authors: Sun-ha Hong
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Post-truth tells the story of a public descending into unreason, aided and abetted by platforms and other data-driven systems. But this apparent collapse of epistemic consensus is, I argue, also dominated by loud and aggressive commitment to the idea of facts and Reason – a site where an imagined modern past is being pillaged for vestigial legitimacy. This article identifies two common practices of such reappropriation and mythologisation. (1) Fact signalling involves performative invocations of facts and Reason, which are then weaponised to discredit communicative rivals and establish affective solidarity. This is often closely tied to (2) fact nostalgia: the cultivation of an imagined past when ‘facts were facts’ and we, the good liberal subjects, could recognise facts when we saw them. Both tendencies are underwritten by a myth of connection: the still enduring narrative that maximising the circulation of information regardless of provenance or meaning will eventually yield a more rational public – even as data-driven systems tend to undermine the very conditions for such a public. Drawing on examples from YouTube-amplified ‘alternative influencers’ in the American right, and the normative discourses around fact-checking practices, I argue that this continued reliance on the vestigial authority of the modern past is a pernicious obstacle in normative debates around data-driven publics, keeping us stuck on the same dead-end scripts of heroically suspicious individuals and ignorant, irrational masses.
Citation: Big Data & Society
PubDate: 2023-03-20T02:21:00Z
DOI: 10.1177/20539517231164118
Issue No: Vol. 10, No. 1 (2023)
- Imaginaries of better administration: Renegotiating the relationship
between citizens and digital public power
Authors: Terhi Esko, Riikka Koulu
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
This article investigates future visions of digital public administration as they appear within a particular regulatory process that aims to enable automated decision-making (ADM) in public administration in Finland. By drawing on science and technology studies, public administration studies, and socio-legal studies we analyze law in the making and identify four imaginaries of public digital administration: understandable administration, self-monitoring administration, adaptive administration, and responsible administration. We argue that digital administration is seen from the perspective of public authorities serving their current needs of legitimizing existing automation practices. While technology is pictured as unproblematic, the citizen perspective is missing. We conclude that the absence of an in-depth understanding of the diverse needs of citizens raises the question whether the relationship between public power and citizens is becoming a one-way street despite of the public administration ideals that express values of citizen engagement.
Citation: Big Data & Society
PubDate: 2023-03-20T02:20:20Z
DOI: 10.1177/20539517231164113
Issue No: Vol. 10, No. 1 (2023)
- On samples, data, and their mobility in biobanking: How imagined travels
help to relate samples and data
Authors: Ingrid Metzler, Lisa-Maria Ferent, Ulrike Felt
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Biobanking involves the assembling, curating, and distributing of samples and data. While relations between samples and data are often taken as defining properties of biobanking, several studies have pointed to the challenges in relating them in practice. This article investigates how samples and data are curated, connected, and made mobile in practice. Building on an analysis of data collected at five hospital-based biobanks in Austria, the article describes and compares biobanking in three types of biobank collections: ‘departmental collections’, ‘project-specific collections’ and ‘hospital-wide collections’. It draws attention to the invisible work going into this infrastructure and highlights the central role of visions to make samples and data travel to a different location and thus support biomedical research. It shows that while visions of future travels are often epistemologically uncertain, they are informed by social ties and relationships between the collectives involved in the curation of samples and data on the one hand and the imagined users on the other. Finally, we point to the importance that policy actors in this domain consider the aspects we identified—and, in particular, reflect the temporalities inherent in such a research infrastructure.
Citation: Big Data & Society
PubDate: 2023-03-20T02:19:07Z
DOI: 10.1177/20539517231158635
Issue No: Vol. 10, No. 1 (2023)
- European Search' How to counter-imagine and counteract hegemonic search
with European search engine projects
Authors: Astrid Mager
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
This article investigates how developers of alternative search engines challenge increasingly corporate imaginaries of digital futures by building out counter-imaginaries of search engines devoted to social values instead of mere profit maximization. Drawing on three in-depth case studies of European search engines, it analyzes how search engine developers counter-imagine hegemonic search, what social values support their imaginaries, and how they are intertwined with their sociotechnical practices. This analysis shows that notions like privacy, independence, and openness appear to be fluid, context-dependent, and changing over time, leading to a certain “value pragmatics” that allows the projects to scale beyond their own communities of practice. It further shows how European values, and broader notions of Europe as “unified or pluralistic,” are constructed and co-produced with developers’ attempts to counter-imagine and counteract hegemonic search. To conclude, I suggest three points of intervention that may help alternative search engine projects, and digital technologies more generally, to not only make their counter-imaginaries more powerful, but also acquire the necessary resources to build their technologies and infrastructures accordingly. I finally discuss how “European values,” in all their richness and diversity, can contribute to this undertaking.
Citation: Big Data & Society
PubDate: 2023-03-15T06:24:30Z
DOI: 10.1177/20539517231163173
Issue No: Vol. 10, No. 1 (2023)
- Virtual state, where are you' A literature review, framework and agenda
for failed digital transformation
Authors: Shirley Kempeneer, Frederik Heylen
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The users, sensors and networks of the Internet of Things generate huge amounts of data. Given the sophisticated (artificially intelligent) algorithms, computing power and software available, we would expect governments to have successfully completed their digital transformation into Jane Fountain's (2001) ‘Virtual State’. In practice, despite heavy investments, governments often fail to enact new digital technologies in an efficient, appropriate or fair way. This article provides an overview of techno-rational and socio-political failures and solutions at the macro-, meso- and micro-level to support digital transformation. The reviewed articles suggest a modest approach to digital transformation, with an emphasis on high-quality in-house IT infrastructure and expertise, but also better collaborative networks and strong leadership ensuring human oversight.
Citation: Big Data & Society
PubDate: 2023-03-15T06:23:51Z
DOI: 10.1177/20539517231160528
Issue No: Vol. 10, No. 1 (2023)
- Based and confused: Tracing the political connotations of a memetic phrase
across the web
Authors: Sal Hagen, Daniël de Zeeuw
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Current research on the weaponisation of far-right discourse online has mostly focused on the dangers of normalising hate speech. However, this often operates on questionable assumptions about how far-right terms retain problematic meanings over time and across different platforms. Yet contextual meaning-change, we argue, is key to assessing the normalisation of problematic but fuzzy terms as they spread across the Web. To redress this, our article traces the changing meaning of the term based, a word that was appropriated from Black Twitter to become a staple of online far-right slang in the mid-2010s. Through a quali-quantitative cross-platform approach, we analyse the evolution of the term between 2010 and 2021 on Twitter, Reddit and 4chan. We find that while the far right meaning of based partially survived, its meaning changed and was rendered diffuse as it was adopted by other communities, afforded by a repurposable kernel of meaning to based as ‘not caring about what other people think’ and ‘being true to yourself’ to which different (political) connotations were attached. This challenges the understanding of far-right memes and hate speech as carrying a single and persistent problematic message, and instead emphasises their varied meanings and subcultural functions within specific online communities.
Citation: Big Data & Society
PubDate: 2023-03-14T09:00:21Z
DOI: 10.1177/20539517231163175
Issue No: Vol. 10, No. 1 (2023)
- All WARC and no playback: The materialities of data-centered web archives
research
Authors: Emily Maemura
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
This paper examines the Web ARChive (WARC) file format, revealing how the format has come to play a central role in the development and standardization of interoperable tools and methods for the international web archiving community. In the context of emerging big data approaches, I consider the sociotechnical relationships between material construction of data and information infrastructures for collecting and research. Analysis is inspired by Star and Griesemer's historical case of the Museum of Vertebrate Zoology which reveals how boundary objects and methods standardization are used to enroll actors in the work of collecting for natural history. I extend these concepts by pairing them with frameworks for studying digital materiality and the representational qualities of data artifacts. Through examples drawn from fieldwork observations studying two data-centered research projects, I consider how the materiality of the WARC format influences research methods and approaches to data extraction, selection, and transformation. Findings identify three modalities researchers use to configure WARC data for researcher needs: using indexes to support search queries, constructing derivative formats designed for certain types of analysis, and generating custom-designed datasets tailored for specific research purposes. Findings additionally reveal similarities in how these distinct methods approach automated data extraction by relying upon the WARC's standardized metadata elements. By interrogating whose information needs are being met and taken into account in the design of the WARC's underlying information representation, I reveal effects on the emerging field of web history, and consider alternative approaches to knowledge production with archived web data.
Citation: Big Data & Society
PubDate: 2023-03-14T06:22:18Z
DOI: 10.1177/20539517231163172
Issue No: Vol. 10, No. 1 (2023)
- Editorial introduction: Towards a machinic anthropology
Authors: Morten Axel Pedersen
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue is to explore what an integration or even fusion between anthropology and data science might look like. Going beyond existing work on the complementarity between ‘thick’ qualitative and ‘big’ quantitative data, the ambition is to unsettle and push established disciplinary, methodological and epistemological boundaries by creatively and critically probing various computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. Can ethnographic and other qualitative data and methods be integrated with natural language processing tools and other machine-learning techniques, and if so, to what effect' Does the rise of data science allow for the realization of Levi-Strauss’ old dream of a computational structuralism, and even if so, should it' Might one even go as far as saying that computers are now becoming agents of social scientific analysis or even thinking: are we about to witness the birth of distinctly anthropological forms of artificial intelligence' By exploring these questions, the hope is not only to introduce scholars and students to computational anthropological methods, but also to disrupt predominant norms and assumptions among computational social scientists and data science writ large.
Citation: Big Data & Society
PubDate: 2023-03-06T06:33:22Z
DOI: 10.1177/20539517231153803
Issue No: Vol. 10, No. 1 (2023)
- Disability data and its situational and contextual irrationalities in the
Global South
Authors: Abdul Rohman, Dyah Pitaloka, Erlina Erlina, Duy Dang, Ade Prastyani
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The inconsistent implementation of disability rights in crisis responses such as the recent COVID-19 pandemic has illuminated the double difficulty that persons with disabilities (PwD) must face. Ableism remains the basis for pandemic responses, leading to a range of irrationalities in collecting and using disability data during critical times. This commentary identifies situational and contextual rationalities in disability data collection and use in Global South. Through vignettes from Indonesia and Vietnam, this commentary illuminates the socio-technical and cultural infrastructure that perpetuates the obscurity of disability rights in the pandemic responses in, respectively, the largest democratic and socialist-communist countries in Southeast Asia. In addition to better listening to the voice of PwD, stronger engagement of organizations of PwD in policy making and programming is advocated for enabling more equitable pandemic preparedness, response, and recovery plans to manifest in future.
Citation: Big Data & Society
PubDate: 2023-03-02T05:56:12Z
DOI: 10.1177/20539517231160523
Issue No: Vol. 10, No. 1 (2023)
- Designing privacy in personalized health: An empirical analysis
Authors: Thibaud Deruelle, Veronika Kalouguina, Philipp Trein, Joël Wagner
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
A crucial challenge for personalized health is the handling of individuals’ data and specifically the protection of their privacy. Secure storage of personal health data is of paramount importance to convince citizens to collect personal health data. In this survey experiment, we test individuals’ willingness to produce and store personal health data, based on different storage options and whether this data is presented as common good or private good. In this paper, we focus on the nonmedical context with two means to self-produce data: connected devices that record physical activity and genetic tests that appraise risks of diseases. We use data from a survey experiment fielded in Switzerland in March 2020 and perform regression analyses on a representative sample of Swiss citizens in the French- and German-speaking cantons. Our analysis shows that respondents are more likely to use both apps and tests when their data is framed as a private good to be stored by individuals themselves. Our results demonstrate that concerns regarding the privacy of personal heath data storage trumps any other variable when it comes to the willingness to use personalized health technologies. Individuals prefer a data storage format where they retain control over the data. Ultimately, this study presents results susceptible to inform decision-makers in designing privacy in personalized health initiatives.
Citation: Big Data & Society
PubDate: 2023-03-02T05:55:53Z
DOI: 10.1177/20539517231158636
Issue No: Vol. 10, No. 1 (2023)
- Judgments as bulk data
Authors: Václav Janeček
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Should court judgments be publicly available for text and data mining purposes' This article shows that the arguments for and against access to judgments conflate different understandings of what judgments are. On one view, judgments are seen as a ‘jurisprudential’ category, whereas the other view regards them as something ‘factual’. Once it is understood that these views and the claims based on them do not fight over the same territory, it should be easier to make judgments more widely available, including for the purposes of computational analysis of judgments as bulk data. The purpose of this article is to help to clear the ground for the debate around access to judgments as bulk data and highlight some relevant considerations for the preferred licencing regime concerning judgments.
Citation: Big Data & Society
PubDate: 2023-02-28T05:37:42Z
DOI: 10.1177/20539517231160527
Issue No: Vol. 10, No. 1 (2023)
- Expansive and extractive networks of Web3
Authors: Jathan Sadowski, Kaitlin Beegle
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The self-proclaimed usurper of Web 2.0, Web3 quickly became the center of attention. Not long ago, the public discourse was saturated with projects, promises, and peculiarities of Web3. Now the spotlight has swung around to focus on the many faults, failures, and frauds of Web3. The cycles of technological trends and investment bubbles seem to be accelerating in such a way as to escape any attempt at observing them in motion before they crash, and then everybody moves on to the next thing. Importantly, Web3 was not an anomaly or curiosity in the broader tech industry. It articulates patterns that existed before Web3 and will exist after. Web3 should be understood as a case study of innovation within the dominant model of Silicon Valley venture capitalism. Our focus in this article is on understanding how the movement around Web3 formed through an interplay between (1) normative concepts and contestations related to ideas of “decentralization” and (2) political economic interests and operations related to the dynamics of fictitious capital. By offering a critical analysis of Web3, our goal is also to show how any even potentially progressive (or as we call them “expansive”) forms of Web3 development struggle for success, recognition, and attention due to the wild excesses of hype and investment devoted to “extractive” forms of Web3. In the process, they provide us a better view of how different arrangements of technopolitics can exist at the same time, side-by-side, in complicated ways.
Citation: Big Data & Society
PubDate: 2023-02-28T05:37:22Z
DOI: 10.1177/20539517231159629
Issue No: Vol. 10, No. 1 (2023)
- The world wide web of carbon: Toward a relational footprinting of
information and communications technology's climate impacts
Authors: Anne Pasek, Hunter Vaughan, Nicole Starosielski
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The climate impacts of the information and communications technology sector—and Big Data especially—is a topic of growing public and industry concern, though attempts to quantify its carbon footprint have produced contradictory results. Some studies argue that information and communications technology's global carbon footprint is set to rise dramatically in the coming years, requiring urgent regulation and sectoral degrowth. Others argue that information and communications technology's growth is largely decoupled from its carbon emissions, and so provides valuable climate solutions and a model for other industries. This article assesses these debates, arguing that, due to data frictions and incommensurate study designs, the question is likely to remain irresolvable at the global scale. We present six methodological factors that drive this impasse: fraught access to industry data, bottom-up vs. top-down assessments, system boundaries, geographic averaging, functional units, and energy efficiencies. In response, we propose an alternative approach that reframes the question in spatial and situated terms: A relational footprinting that demarcates particular relationships between elements—geographic, technical, and social—within broader information and communications technology infrastructures. Illustrating this model with one of the global Internet's most overlooked components—subsea telecommunication cables—we propose that information and communications technology futures would be best charted not only in terms of quantified total energy use, but in specifying the geographical and technical parts of the network that are the least carbon-intensive, and which can therefore provide opportunities for both carbon reductions and a renewed infrastructural politics. In parallel to the politics of (de)growth, we must also consider different network forms.
Citation: Big Data & Society
PubDate: 2023-02-27T07:52:48Z
DOI: 10.1177/20539517231158994
Issue No: Vol. 10, No. 1 (2023)
- The ethical dimensions of Google autocomplete
Authors: Rosie Graham
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Citation: Big Data & Society
PubDate: 2023-02-24T06:32:54Z
DOI: 10.1177/20539517231156518
Issue No: Vol. 10, No. 1 (2023)
- Web3 as ‘self-infrastructuring’: The challenge is how
Authors: Kelsie Nabben
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The term ‘Web3’ refers to the practices of participating in digital infrastructures through the ability to read, write and coordinate digital assets. Web3 is hailed as an alternative to the failings of big tech, offering a participatory mode of digital self-organizing and shared ownership of digital infrastructure through software-encoded governance rules and participatory practices. Yet, very few analytical frameworks have been presented in academic literature by which to approach Web3. This piece draws on the theoretical lens of infrastructure studies to offer an analytical framework to approach the emergent field of Web3 as an exploration in ‘how to infrastructure’ through prefigurative self-infrastructuring. Drawing on qualitative examples from digital ethnographic methods, I demonstrate how the origins of Web3 reveal the intentions of its creators as a political tool of prefiguration, yet its practices reveal the inherent tension of expressing these ideals in coherent technical and institutional infrastructure. Thus, I argue that one of the fundamental challenges Web3 is negotiating through technical and governance experiments is ‘how to self-infrastructure'’.
Citation: Big Data & Society
PubDate: 2023-02-23T06:21:42Z
DOI: 10.1177/20539517231159002
Issue No: Vol. 10, No. 1 (2023)
- Ecological ethics and the smart circular economy
Authors: Rolien Hoyng
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The corporate discourse on the circular economy holds that the growth of the electronics industry, driven by continuous innovation, does not imperil ecological sustainability. To achieve sustainable growth, its advocates propose optimizing recycling by means of artificial intelligence and sets of interrelated datacentric and algorithmic technologies. Drawing on critical data and algorithm studies, theories of waste, and empirical research, this paper investigates ecological ethics in the context of the datacentric and algorithmically mediated circular economy. It foregrounds the indeterminate and fickle material nature of waste as well as the uncertainties inherent in, and stemming from, datafication and computation. My question is: how do the rationalities, affordances, and dispositions of datacentric and algorithmic technologies perform and displace notions of corporate responsibility and transparency' In order to answer this question, I compare the smart circular economy to the informal recycling practices that it claims to replace, and I analyze relations between waste matter and data as well as distributions of agency. Specifically, I consider transitions and slippages between response-ability and responsibility. Conceptually, I bring process-relation or immanence-based philosophies such as Bergson's and Deleuze's into a debate about relations between waste matter and data and the ambition of algorithmic control over waste. My aim is not to demand heightened corporate responsibility enacted through control but to rethink responsibility in the smart circular economy along the lines of Amoore's cloud ethics to carve out a position of critique beyond either a deontological perspective that reinforces corporate agency or new-materialist denunciation of the concept.
Citation: Big Data & Society
PubDate: 2023-02-23T06:21:22Z
DOI: 10.1177/20539517231158996
Issue No: Vol. 10, No. 1 (2023)
- The right to information or data sovereignty' Sending unsolicited messages
to Russians about the war in Ukraine
Authors: Yao-Tai Li, Katherine Whitworth
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The Russian government's narrative about the Russia-Ukraine war has raised concerns about disinformation, fake news and freedom of information. In response, websites have been developed that allow people across the world to call or send emails and texts with information about the war to individuals based in Russia. To facilitate this person-to-person communication between strangers, automated data processing has been used to collect personal data from the internet and compile it into publicly accessible mailing lists. This side-stepping of consent coupled with the nature of information being transmitted and the motivation behind its transmission poses important questions of an ethical nature: What is an appropriate balance between the data subjects’ right to freedom of information and their right to privacy' Can data processing without the consent of the data subject be justified in certain circumstances' This commentary does not seek to provide definitive answers to these questions, rather it canvases some key issues in the hope of starting further dialogue on the topic.
Citation: Big Data & Society
PubDate: 2023-02-23T06:20:52Z
DOI: 10.1177/20539517231156123
Issue No: Vol. 10, No. 1 (2023)
- Social media advertising for clinical studies: Ethical and data protection
implications of online targeting
Authors: Rainer Mühlhoff, Theresa Willem
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Social media advertising has revolutionised the advertising world by providing data-driven targeting methods. One area where social media advertising is just gaining a foothold is in the recruitment of clinical study participants. Here, as everywhere, social media advertising promises more yield per money spent because the technology can better reach highly specialised groups. In this article, we point out severe societal risks posed by advertising for clinical studies on social media. We show that social media advertising for clinical studies in many cases violates the privacy of individual users (R1), creates collective privacy risks by helping platform companies train predictive models of medical information that can be applied to all their users (R2), exploits the weaknesses of existing guidelines in (biomedical) research ethics (R3) and is detrimental to the quality of (biomedical) research (R4). We argue that the well-intentioned promises, which are often associated with the use of social media advertising for clinical studies, are untenable from a balanced point of view. Consequently, we call for updates of research ethics guidelines and better regulation of Big Data and inferential analytics. We conclude that social media advertising – especially with vulnerable patient populations – is not suitable as a recruitment tool for clinical studies as long as the processing of (even anonymised) social media usage data and the training of predictive models by data analytics and artificial intelligence companies is not sufficiently regulated.
Citation: Big Data & Society
PubDate: 2023-02-22T06:53:01Z
DOI: 10.1177/20539517231156127
Issue No: Vol. 10, No. 1 (2023)
- Google Search and the creation of ignorance: The case of the climate
crisis
Authors: Jutta Haider, Malte Rödl
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The article examines the relationship between commercial search engines, using Google Search as an example, and various forms of ignorance related to climate change. It draws on concepts from the field of agnotology to explore how environmental ignorances, and specifically related to the climate crisis, are shaped at the intersection of the logics of Google Search, everyday life and civil society/politics. Ignorance refers to a multi-facetted understanding of the culturally contingent ways in which something may not be known. Two research questions are addressed: How are environmental ignorances, and in particular related to the climate crisis, shaped at the intersection of the logics of Google Search, everyday life and civil society/politics' In what ways can we conceptualise Google's role as configured into the creation of ignorances' The argument is made through four vignettes, each of which explores and illustrates how Google Search is configured into a different kind of socially produced ignorance: (1) Ignorance through information avoidance: climate anxiety; (2) Ignorance through selective choice: gaming search terms; (3) Ignorance by design: algorithmically embodied emissions; (4) Ignorance through query suggestions: directing people to data voids. The article shows that while Google Search and its underlying algorithmic and commercial logic pre-figure these ignorances, they are also co-created and co-maintained by content producers, users and other human and non-human actors, as Google Search has become integral of social practices and ideas about them. The conclusion draws attention to a new logic of ignorance that is emerging in conjunction with a new knowledge logic.
Citation: Big Data & Society
PubDate: 2023-02-21T05:08:39Z
DOI: 10.1177/20539517231158997
Issue No: Vol. 10, No. 1 (2023)
- Because the machine can discriminate: How machine learning serves and
transforms biological explanations of human difference
Authors: Jeffrey W. Lockhart
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Research on scientific/intellectual movements, and social movements generally, tends to focus on resources and conditions outside the substance of the movements, such as funding and publication opportunities or the prestige and networks of movement actors. Drawing on Pinch’s theory of technologies as institutions, I argue that research methods can also serve as resources for scientific movements by institutionalizing their ideas in research practice. I demonstrate the argument with the case of neuroscience, where the adoption of machine learning changed how scientists think about measurement and modeling of group difference. This provided an opportunity for members of the sex difference movement by offering a ‘truly categorical’ quantitative methodology that aligned more closely with their understanding of male and female brains and bodies as categorically distinct. The result was a flurry of publications and symbiotic relationships with other researchers that rescued a scientific movement which had been growing increasingly untenable under the prior methodological regime of univariate, frequentist analyses. I call for increased sociological attention to the inner workings of technologies that we typically black box in light of their potential consequences for the social world. I also suggest that machine learning in particular might have wide-reaching implications for how we conceive of human groups beyond sex, including race, sexuality, criminality, and political position, where scientists are just beginning to adopt its methods.
Citation: Big Data & Society
PubDate: 2023-02-21T05:07:40Z
DOI: 10.1177/20539517231155060
Issue No: Vol. 10, No. 1 (2023)
- The multifaceted and situated data center imaginary of Dutch Twitter
Authors: Karin van Es, Daan van der Weijden, Jeroen Bakker
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Data centers are material structures that take up space, use resources like water and energy, and possess a large carbon footprint. This paper examines the broader long-term discussion around data centers during the period 2020–2022 in the Dutch Twittersphere. Through an analysis of tweets and images, it identifies and reflects on the communities active in the discussion and the range of visions and imaginaries of data centers they produce. Unpacking these tweets and images over time traces not only the emergence of a ‘reactive imaginary’, critical of the promises of information technology (IT) industry and (local) governments, but also the blind spots of the discussion. It furthermore reveals an important role for journalism in the discussion by questioning the claims of the industry and contributing to a ‘visibility expansion’ of data center’s impact on Earth's resources. The paper shows the multifaceted and situated nature of imaginaries and their role in shaping decision-making and policy.
Citation: Big Data & Society
PubDate: 2023-02-16T06:18:24Z
DOI: 10.1177/20539517231155064
Issue No: Vol. 10, No. 1 (2023)
- European artificial intelligence policy as digital single market making
Authors: Troels Krarup, Maja Horst
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Rapid innovation in digital services relying on artificial intelligence (AI) challenges existing regulations across a wide array of policy fields. The European Union (EU) has pursued a position as global leader on ethical AI regulation in explicit contrast to US laissez-faire and Chinese state surveillance approaches. This article asks how the seemingly heterogeneous approaches of market making and ethical AI are woven together at a deeper level in EU regulation. Combining quantitative analysis of all official EU documents on AI with in-depth reading of key reports, communications, and legislative corpora, we demonstrate that single market integration constitutes a fundamental but overlooked engine and structuring principle of new AI regulation. Under the influence of this principle, removing barriers to competition and the free flow of data, on the one hand, and securing ethical and responsible AI, on the other hand, are seen as compatible and even mutually reinforcing.
Citation: Big Data & Society
PubDate: 2023-02-14T07:19:46Z
DOI: 10.1177/20539517231153811
Issue No: Vol. 10, No. 1 (2023)
- How platforms govern: Social regulation in digital capitalism
Authors: Petter Törnberg
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
The rise of digital platforms has in recent years redefined contemporary capitalism—provoking discussions on whether platformization should be understood as bringing an altogether new form of capitalism, or as merely a continuation and intensification of existing neoliberal trends. This paper draws on regulation theory to examine social regulation in digital capitalism, arguing for understanding digital capitalism as continuities of existing capitalist trends coming to produce discontinuities. The paper makes three main arguments. First, it situates digital capitalism as a continuation of longer running post-Fordist trends of financialization, digitalization, and privatization—converging in the emergence of digital proprietary markets, owned and regulated by transnational platform companies. Second, as the platform model is founded on monopolizing regulation, platforms come into direct competition with states and public institutions, which they pursue through a set of distinct technopolitical strategies to claim power to govern—resulting in a geographically variegated process of institutional transformation. Third, while the digital proprietary markets are continuities of existing trends, they bring new pressures and affordances, thus producing discontinuities in social regulation. We examine such discontinuities in relation to three aspects of social regulation: (a) from neoliberalism to techno-feudalism; (b) from Taylorist hierarchies toward algorithmic herds and technoliberal subjectivity; and (c) from postmodernity toward an automated consumer culture.
Citation: Big Data & Society
PubDate: 2023-02-13T05:04:33Z
DOI: 10.1177/20539517231153808
Issue No: Vol. 10, No. 1 (2023)
- Learning machine learning: On the political economy of big tech's online
AI courses
Authors: Inga Luchs, Clemens Apprich, Marcel Broersma
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives.
Citation: Big Data & Society
PubDate: 2023-02-08T05:58:53Z
DOI: 10.1177/20539517231153806
Issue No: Vol. 10, No. 1 (2023)
- Contextualizing realism: An analysis of acts of seeing and recording in
Digital Twin datafication
Authors: Paulan Korenhof, Else Giesbers, Janita Sanderse
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Digital Twins are conceptualized as real-time digital representations of real-life physical entities or systems. They are explored for a wide array of societal implementations, and in particular to help address fundamental societal challenges. As accurate digital equivalents of their real-life twin, Digital Twins substitute their physical twin in knowledge production and decision-making processes. They raise high expectations: they are expected to produce new knowledge, expose issues early, predict future behavior, and help to optimize the physical twin. Data play a key role here because they form the building blocks from which the Digital Twin representation is created. However, data are not neutral phenomena but products of human-technology interaction. In this article, we therefore raise the question of how a Digital Twin data collection is created, and what implications does this have for Digital Twins' To answer this question, we explore the data collection process in three cases of Digital Twin development at a university. Connecting to Jasanoff's theoretical framework of regimes of sight, we approach the creation of a data collection as acts of seeing and recording that influence how reality is represented in data, as well as give a certain legitimacy and authority to the data collection. By examining the acts of seeing and recording and their respective roles in producing the data collection, we provide insight into the struggles of representation in Digital Twins and their implications.
Citation: Big Data & Society
PubDate: 2023-02-08T05:08:40Z
DOI: 10.1177/20539517231155061
Issue No: Vol. 10, No. 1 (2023)
- Google, data voids, and the dynamics of the politics of exclusion
Authors: Ov Cristian Norocel, Dirk Lewandowski
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
This study deploys a critical approach to big data analytics to gauge the tentative contours of data voids in Google searches that reflect extreme-right dynamics of exclusion in the aftermath of the 2015 humanitarian crisis in Europe. The study adds complexity to the analysis of data voids, expanding the framework of investigation outside the USA context by concentrating on Germany and Sweden. Building on previous big data analytics addressing the politics of exclusion, the study proposes a catalogue of queries concerning the issue of migration in both Germany and Sweden on a continuum from mainstream to extreme-right vocabularies. This catalogue of queries enables specific and localized queries to identify data voids. The results show that a search engine's reliance on source popularity may lead to extreme-right sources appearing in top positions. Furthermore, using platforms for user-generated content provides a way for localized queries to gain top positions.
Citation: Big Data & Society
PubDate: 2023-02-06T06:58:32Z
DOI: 10.1177/20539517221149099
Issue No: Vol. 10, No. 1 (2023)
- The role of evidence-based misogyny in antifeminist online communities of
the ‘manosphere’
Authors: Ann-Kathrin Rothermel
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
In recent years, there have been a growing number of online and offline attacks linked to a loosely connected network of misogynist and antifeminist online communities called ‘the manosphere’. Since 2016, the ideas spread among and by groups of the manosphere have also become more closely aligned with those of other Far-Right online networks. In this commentary, I explore the role of what I term ‘evidence-based misogyny’ for mobilization and radicalization into the antifeminist and misogynist subcultures of the manosphere. Evidence-based misogyny is a discursive strategy, whereby members of the manosphere refer to (and misinterpret) knowledge in the form of statistics, studies, news items and pop-culture and mimic accepted methods of knowledge presentation to support their essentializing, polarizing views about gender relations in society. Evidence-based misogyny is a core aspect for manosphere-related mobilization as it provides a false sense of authority and forges a collective identity, which is framed as a supposed ‘alternative’ to mainstream gender knowledge. Due to its core function to justify and confirm the misogynist sentiments of users, evidence-based misogyny serves as connector between the manosphere and both mainstream conservative as well as other Far-Right and conspiratorial discourses.
Citation: Big Data & Society
PubDate: 2023-02-06T06:57:46Z
DOI: 10.1177/20539517221145671
Issue No: Vol. 10, No. 1 (2023)
- Formally comparing topic models and human-generated qualitative coding of
physician mothers’ experiences of workplace discrimination
Authors: Adam S Miner, Sheridan A Stewart, Meghan C Halley, Laura K Nelson, Eleni Linos
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Differences between computationally generated and human-generated themes in unstructured text are important to understand yet difficult to assess formally. In this study, we bridge these approaches through two contributions. First, we formally compare a primarily computational approach, topic modeling, to a primarily human-driven approach, qualitative thematic coding, in an impactful context: physician mothers’ experience of workplace discrimination. Second, we compare our chosen topic model to a principled alternative topic model to make explicit study design decisions meriting consideration in future research. By formally contrasting computationally generated (i.e. topic modeling) and human-generated (i.e. thematic coding) knowledge, we shed light on issues of interest to several audiences, notably computational social scientists who wish to understand study design tradeoffs, and qualitative researchers who may wish to leverage computational methods to improve the speed and reproducibility of labor-intensive coding. Although useful in other domains, we highlight the value of fast, reproducible methods to better understand experiences of workplace discrimination.
Citation: Big Data & Society
PubDate: 2023-01-25T05:51:09Z
DOI: 10.1177/20539517221149106
Issue No: Vol. 10, No. 1 (2023)
- Manipulative tactics are the norm in political emails: Evidence from 300K
emails from the 2020 US election cycle
Authors: Arunesh Mathur, Angelina Wang, Carsten Schwemmer, Maia Hamin, Brandon M Stewart, Arvind Narayanan
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
We collect and analyze a corpus of more than 300,000 political emails sent during the 2020 US election cycle. These emails were sent by over 3000 political campaigns and organizations including federal and state level candidates as well as Political Action Committees. We find that in this corpus, manipulative tactics—techniques using some level of deception or clickbait—are the norm, not the exception. We measure six specific tactics senders use to nudge recipients to open emails. Three of these tactics—“dark patterns”—actively deceive recipients through the email user interface, for example, by formatting “from:” fields so that they create the false impression the message is a continuation of an ongoing conversation. The median active sender uses such tactics 5% of the time. The other three tactics, like sensationalistic clickbait—used by the median active sender 37% of the time—are not directly deceptive, but instead, exploit recipients’ curiosity gap and impose pressure to open emails. This can further expose recipients to deception in the email body, such as misleading claims of matching donations. Furthermore, by collecting emails from different locations in the US, we show that senders refine these tactics through A/B testing. Finally, we document disclosures of email addresses between senders in violation of privacy policies and recipients’ expectations. Cumulatively, these tactics undermine voters’ autonomy and welfare, exacting a particularly acute cost for those with low digital literacy. We offer the complete corpus of emails at https://electionemails2020.org for journalists and academics, which we hope will support future work.
Citation: Big Data & Society
PubDate: 2023-01-23T08:30:19Z
DOI: 10.1177/20539517221145371
Issue No: Vol. 10, No. 1 (2023)
- A critical analysis of digital phenotyping and the neuro-digital complex
in psychiatry
Authors: Rodrigo De La Fabián, Álvaro Jiménez-Molina, Francisco Pizarro Obaid
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
This article critically examines the emergence and uses of digital phenotyping in contemporary psychiatry. From an analysis of its discourses and practices, we show that digital phenotyping diffusion is directly related to its promise to solve some of the major impasses of the so-called "neuro-turn" in contemporary psychiatry. However, more than a new tool to address old objects of pre-digital psychiatry, we consider digital phenotyping as participating from a new onto-epistemological matrix, the “neuro-digital complex,” which entails the redefinition of psychiatric objects (e.g., brain and mind), diagnostic categories and procedures, subjectivities (e.g., users of mental health apps), and the emergence of a new regime of truth which promises to reveal the neuropsychological core at the individual scale. Despite this techno-utopia, digital phenotyping does not produce neutral mirrors for self-knowledge. We show that it resorts to population statistics, grounded truth data sets built with pre-digital neuropsychological assumptions, and human categorization processes. Nevertheless, we propose not to approach this gap as a misleading ideological fact but to emphasize its productive possibilities. From this perspective, the gap becomes the measure between whom we think we are and who we really are, working as a guide to conduct our lives in neuropsychological terms. Thus, we conclude that, rather than providing personalized diagnoses and treatments, digital phenotyping produces individualized pathways to normalization and neuropsychologization.
Citation: Big Data & Society
PubDate: 2023-01-23T06:42:53Z
DOI: 10.1177/20539517221149097
Issue No: Vol. 10, No. 1 (2023)
- Surface and Sublevel Hate
Authors: Luke Munn
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
On the face of it, contemporary “alt-tech” platforms appear more moderate than legacy hate havens. Yet it's also clear that virulent hate in the form of misogyny, white supremacy, and xenophobia has not disappeared. Probing this tension, this article conceptualizes two forms of hate: Surface “Hate” (moderate content that is highly visible and easily accessible) and Sublevel Hate (explicit content that is more marginal and less discernible). These terms are illustrated by examining several viral videos on Rumble. This twinned mechanism explains how alt-tech platforms can be both accessible and extreme at the same time. Stratified hate is strategic, heightening the appeal and durability of online communities. Recognizing this dangerous dynamic is key for interventions seeking to counter it.
Citation: Big Data & Society
PubDate: 2023-01-20T05:45:57Z
DOI: 10.1177/20539517221148136
Issue No: Vol. 10, No. 1 (2023)
- Machine learning and the politics of synthetic data
Authors: Benjamin N Jacobsen
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events' Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society.
Citation: Big Data & Society
PubDate: 2023-01-17T01:07:16Z
DOI: 10.1177/20539517221145372
Issue No: Vol. 10, No. 1 (2023)
- Short-circuiting biology: Digital phenotypes, digital biomarkers, and
shifting gazes in psychiatry
Authors: Shai Mulinari
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Digital phenotyping is a rapidly growing research field promising to transform how psychiatry measures, classifies, predicts, and explains human behavior. This article advances the social-scientific examination of digital phenotyping's epistemology and knowledge claims. Drawing on the notion of a “neuromolecular gaze” in psychiatry since the 1960s, it suggests that digital phenotyping concerns a new psychiatric gaze—the “digital gaze.” Rather than privileging neuromolecular explanations, the digital gaze privileges the “deep” physiological, behavioral, and social “truths” afforded by digital technologies and big data. The article interrogates two concepts directing the digital gaze: “digital phenotype” and “digital biomarkers.” Both concepts make explicit an epistemic link between “the digital” and “the biological.” The article examines the soundness and construction of this link to, first, offer a “reality check” of digital phenotyping's claims and, second, more clearly delineate and demarcate the digital gaze. It argues there is evidence of significant mis- and overstatements about digital phenotyping's basis in biology, including in much-hyped psychiatric digital biomarker research. Rather than driving the biologization of digital traces, as some have suggested, digital mental health phenotyping so far seems mainly concerned with physiological, behavioral, and social processes that can be surveilled by means of digital devices.
Citation: Big Data & Society
PubDate: 2023-01-13T06:00:09Z
DOI: 10.1177/20539517221145680
Issue No: Vol. 10, No. 1 (2023)
- Ground truth tracings (GTT): On the epistemic limits of machine learning
Authors: Edward B Kang
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
There is a gap in existing critical scholarship that engages with the ways in which current “machine listening” or voice analytics/biometric systems intersect with the technical specificities of machine learning. This article examines the sociotechnical assemblage of machine learning techniques, practices, and cultures that underlie these technologies. After engaging with various practitioners working in companies that develop machine listening systems, ranging from CEOs, machine learning engineers, data scientists, and business analysts, among others, I bring attention to the centrality of “learnability” as a malleable conceptual framework that bends according to various “ground-truthing” practices in formalizing certain listening-based prediction tasks for machine learning. In response, I introduce a process I call Ground Truth Tracings to examine the various ontological translations that occur in training a machine to “learn to listen.” Ultimately, by further examining this notion of learnability through the aperture of power, I take insights acquired through my fieldwork in the machine listening industry and propose a strategically reductive heuristic through which the epistemological and ethical soundness of machine learning, writ large, can be contemplated.
Citation: Big Data & Society
PubDate: 2023-01-09T06:17:28Z
DOI: 10.1177/20539517221146122
Issue No: Vol. 10, No. 1 (2023)
- Digital identity as platform-mediated surveillance
Authors: Silvia Masiero
Abstract: Big Data & Society, Volume 10, Issue 1, January-June 2023.
Digital identity systems are usually viewed as datafiers of existing populations. Yet a platform view finds limited space in the digital identity discourse, with the result that the platform features of digital identity systems are not seen in relation to their surveillance outcomes. In this commentary I illuminate how the core platform properties of digital identity systems afford the undue surveillance of vulnerable groups, leading users into the binary condition of either registering and being profiled, or giving up essential benefits from providers of development programmes. By doing so I contest the “dark side” narrative often applied to digital identity, arguing that, rather than just a side, it is the very inner matter of digital identity platforms that enables surveillance outcomes.
Citation: Big Data & Society
PubDate: 2023-01-03T11:30:54Z
DOI: 10.1177/20539517221135176
Issue No: Vol. 10, No. 1 (2023)
