Data-driven models of governance across borders: Datafication from the local to the global
First Monday

Data-driven models of governance across borders: Datafication from the local to the global by Payal Arora and Hallam Stevens



Abstract
This special issue looks closely at contemporary data systems in diverse global contexts and through this set of papers, highlights the struggles we face as we negotiate efficiency and innovation with universal human rights and social inclusion. The studies presented in these essays are situated in diverse models of policy-making, governance, and/or activism across borders. Attention to big data governance in western contexts has tended to highlight how data increases state and corporate surveillance of citizens, affecting rights to privacy. By moving beyond Euro-American borders — to places such as Africa, India, China, and Singapore — we show here how data regimes are motivated and understood on very different terms.

 


 

Big data’s initial luster seems now to have all but worn off: in 2019, we have come quite a distance from Chris Anderson’s “end of theory” (2008) euphoria for data-driven decision-making. Today, numerous scholars have shown how data-centrism in policy-making and political practice has failed to live up to the hype for a better form of social justice, liberated from human bias. Literature from a range of disciplines has problematized the ways in which data is collected, cleaned, shared, analyzed, and used and shown how big data can foster discrimination, illiberalism, and inequality (for example, Arora, 2016; Crawford, et al., 2014; Dencik, et al., 2016; Graham, et al., 2014; Gitelman, 2013; Iliadis and Russo, 2016; Kitchin, 2014; O’Neil, 2016). No serious scholar today would claim that big data platforms are neutral. The argument has shifted from an intrinsic neutrality within these datafication systems to inscribing the ‘right’ kind of values into these novel modes of governance. This special issue looks closely at contemporary data systems in diverse global contexts and through this set of papers, highlights the struggles we face as we negotiate efficiency and innovation with universal human rights and social inclusion.

Big data — intertwined with narratives of e-government, smart government, open government, and citizen empowerment — continue to offer a seductive set of tools for states and citizens. Big data approaches to governance and governmentality continue to drive efforts to level political and economic playing fields. We are witnessing a number of innovations across the world that are strategically leveraging on digitization to promise more transparent and representative forms of political enactment. These social and political experiments aim to break away from traditional modes of institutional practice and offer instead a more vibrant form of democratic engagement, empowered by the affordances of “datafication.”

The studies presented in these essays are situated in diverse models of policy-making, governance, and/or activism across borders. Attention to big data governance in western contexts has tended to highlight how data increases state and corporate surveillance of citizens, impacting rights to privacy. By moving beyond Euro-American borders — to places such as Africa, India, China, and Singapore — we show here how data regimes are motivated and understood on very different terms. Outside the west, the trade-offs between, for example, privacy and access to vital social services, are constituted in different ways. As such, big data governance systems may be celebrated as liberating innovations, even as they appear to restrict democratic freedoms.

Moving beyond western paradigms also allows us to explore how big data and digitization are not singular or universal objects or practices — their meanings and their significance are highly fluid and varies considerably with the social and political context. The meanings of datafication within the context of China’s social credit system is remarkably different from the meanings of datafication within Europe, for example. By examining local and situated practices of data governance within specific environments, these essays open up a deeper conversation about the highly varied meanings and consequences of datafication. By comparing examples emerging from different national and political circumstances, we hope to illuminate the diverse potentialities and valences of big data. The set of papers in this issue aim to disrupt staid universalisms embedded in popular understandings, expectations and fears of what such data-driven systems can and are doing to our digital and social terrain.

Many of the databased systems we examine here are global in scope and scale, or, at least, are embedded within global networks. As such, we also seek to understand the significance of planetary-scale data and data-practices. What do data practices look like on a global scale? What consequences does such scaling-up have? How can data be governed across borders? Although data are far from universal, when they are shared across global networks, they can often appear and behave as though they are universal. This raises important questions and problems about how databased systems are re-shaping power relations between states, citizens, and corporations.

 


 

To establish a kind of baseline, the special issue opens by considering attitudes toward big data in Europe. René König’s essay examines the role of “citizen conferences” in understanding the public’s view of big data in Germany. These “participatory technology assessments” demonstrated that citizens were concerned about the control of big data (should it be under the control of the government or individuals?), about the need for more education about big data technologies, and the need for more government regulation. Participants expressed, in many ways, traditional liberal democratic views and concerns about these technologies centered on individual rights, individual responsibilities, and education. Their proposed solutions too — more education and more government regulation — fit squarely within western liberal democratic traditions.

In contrast to this, Payal Arora’s essay draws us immediately into the vastly different contexts of data governance in India and China. India’s Aadhaar biometric identification system, through tracking its citizens with iris scanning and other measures, promises to root out corruption and provide social services to those most in need. Likewise, China’s emerging “social credit system,” while having immense potential for increasing citizen surveillance, offers ways of increasing social trust and fostering more responsible social behavior online and offline. Although the potential for authoritarian abuses of both systems is high, Arora focuses on how these technologies are locally understood and lived on an everyday basis, which spans from empowering to oppressing their people. From this perspective, the technologies offer modes of “disrupt[ing] systems of inequality and oppression” that should open up new conversations about what democratic participation can and should look like in China and India.

If China and India offer contrasting non-democratic and democratic cases, we turn next to a context that is neither completely western nor completely non-western, neither completely democratic nor completely liberal. Hallam Stevens’ account of government data in Singapore suggests the very different role that data can play in this unique political and social context. Although the island state’s data.gov.sg participates in global discourses of sharing, “open data,” and transparency, much of the data made available by the government is oriented towards the solution of particular economic and social problems. Ultimately, the ways in which data are presented may contribute to entrenching — rather than undermining or transforming — existing forms of governance. The account of data and its meanings that is offered here once again challenges the notion that such data systems can or should be understood in the same ways that similar systems have been understood in the western world.

If systems such as Aadhaar, “social credit,” and data.gov.sg profess to make citizens and governments more visible and legible, Rolien Hoyng examines what may remain invisible even within highly pervasive data-driven systems. In the world of e-waste, data-driven modes of surveillance and logistics are critical for recycling. But many blind spots remain. Hoyng’s account reminds us that despite the often-supposed all-seeing-ness of big data, we should remain attentive to what escapes the data’s gaze. Here, in midst of datafication, we find “invisibility, uncertainty, and, therewith, uncontrollability.” This points also to the gap between the fantasies of how data-driven systems are supposed to work, and their realization in the world. Such interstices allow individuals — those working with e-waste in Shenzhen or Africa, for example — to find and leverage hidden opportunities. From this perspective, the “blind spots of big data” take on a very different significance.

Big data systems provide opportunities for some, but reduce those for others. Mark Graham and Mohammad Amir Anwar examine what happens when online outsourcing platforms create a “planetary labor market.” Although providing opportunities for many people to make money via their Internet connection, Graham and Anwar’s interviews with workers across sub-Saharan Africa demonstrate how “platform work” alters the balance of power between labor and capital. For many low-wage workers across the globe, the platform- and data-driven planetary labor market means downward pressure on wages, fewer opportunities to collectively organize, less worker agency, and less transparency about the nature of the work itself. Moving beyond bold pronouncements that the “world is flat” and big data as empowering, Graham and Anwar show how data-driven systems of employment can act to reduce opportunities for those residing in the poorest parts of the world. The affordances of data and platforms create a planetary labor market for global capital but tie workers ever-more tightly to their own localities. Once again, the valances of global data systems look very different from this “bottom-up” perspective.

Philippa Metcalfe and Lina Dencik shift this conversation from the global movement of labor to that of people, as they write about the implications of European datafication systems on the governance of refugees entering this region. This work highlights how intrinsic to datafication systems is the classification, coding, and collating of people to legitimize the extent of their belonging in the society they seek to live in. The authors argue that these datafied regimes of power have substantively increased their role in the regulating of human mobility in the guise of national security. These means of data surveillance can foster new forms of containment and entrapment of entire groups of people, creating further divides between “us” and “them.” Through vast interoperable databases, digital registration processes, biometric data collection, and social media identity verification, refugees have become some of the most monitored groups at a global level while at the same time, their struggles remain the most invisible in popular discourse.

 


 

The findings here suggest how the relationship between data and democracy is being re-shaped and re-negotiated by and through new technologies and the practices associated with them. In particular, big data technologies bring into being new modes of representation and participation that challenge existing (and particularly western-liberal) notions of democracy. As such, these papers aim to open up conversations about what forms of governance are emerging in the twenty-first century, even as traditional modes of democracy appear to be failing in the United States and western Europe.

As a whole, this special issue critically assesses the nature and role of these new data-driven practices in the shaping of social and political orders. Each paper here evaluates the particulars of select social innovations posited to strengthen policy-making, citizen engagement, and citizen activism in the digital era. Together, they illuminate the ways in which databases have enabled newer forms of mobilization and solidarity but have equally allowed for novel and hitherto unimagined operations of state and corporate power. As such the papers move beyond the polarizing perspectives that portray data-driven approaches as either triumphant successes or unmitigated failures. We aim for a more nuanced and complex understanding of how a multiplicity of social actors come to play in the makings of public service in the big data era.

Of course, this can hardly be the last word in understanding the global implications of big data governance. As we have constantly reiterated here, we need to pay attention to data and its meanings in more times and in more places. Behind each system lies an ideology that is shaped by the local context and global forces. We need to understand more about the specificities of who is building and using data regimes. What intent drives the making of these systems? Who are they built for and who are they open to? What does the data actually represent? Within what forms of traditional governance are these databased systems embedded within? And what work do they do in constructing and re-constructing notions of democracy, justice, privacy, and representation within their local contexts? Such questions become increasingly salient as data-driven platforms continue to spread and proliferate for the sake of a fair and inclusive social future. End of article

 

About the authors

Payal Arora is the author of several books, including The next billion users: Digital life beyond the West (Cambridge, Mass.: Harvard University Press, 2019). She is an Associate Professor at Erasmus University Rotterdam, the Founder of Catalyst Lab, a digital activism organization, and Section Editor for a University of California Press journal, Global perspectives.
E-mail: arora [at] eshcc [dot] eur [dot] nl

Hallam Stevens is an associate professor of history at Nanyang Technological University in Singapore. He is the author of Life out of sequence: A data-driven history of bioinformatics (Chicago: University of Chicago Press, 2013), Biotechnology and society: An introduction (Chicago: University of Chicago Press, 2016), and the co-editor of Postgenomics: Perspectives on biology after the genome (Durham, N.C.: Duke University Press, 2015).
E-mail: hstevens [at] ntu [dot] edu [dot] sg

 

References

Chris Anderson, 2008. “The end of theory: The data deluge makes the scientific method obsolete,” Wired (23 June), at https://www.wired.com/2008/06/pb-theory/, accessed 5 March 2019.

Payal Arora, 2016. “Bottom of the data pyramid: Big data and the global south,“ International Journal of Communication, volume 10, at https://ijoc.org/index.php/ijoc/article/view/4297, accessed 5 March 2019.

Kate Crawford, Mary L. Gray, and Kate Miltner, 2014. “Critiquing big data: Politics, ethics, epistemology,” International Journal of Communication, volume 8, pp. 1,663–1,672, and at https://ijoc.org/index.php/ijoc/article/view/2167, accessed 5 March 2019.

Lina Dencik, Arne Hintz, and Jonathan Cable, 2016. “Towards data justice? The ambiguity of anti-surveillance resistance in political activism,” Big Data & Society.
doi: https://doi.org/10.1177/2053951716679678, accessed 5 March 2019.

Lisa Gitelman (editor), 2013. “Raw data” is an oxymoron. Cambridge, Mass.: MIT Press.

Mark Graham, Bernie Hogan, Ralph K. Straumann, and Ahmed Medhat, 2014. “Uneven geographies of user-generated information: Patterns of increasing informational poverty,” Annals of the Association of American Geographers, volume 104, number 4, pp. 746–764.
doi: https://doi.org/10.1080/00045608.2014.910087, accessed 5 March 2019.

Andrew Iliadis and Federica Russo, 2016. “Critical data studies: An introduction.” Big Data & Society.
doi: https://doi.org/10.1177/2053951716674238, accessed 5 March 2019.

Rob Kitchin, 2014. The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.

Cathy O’Neil, 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown.

 


Editorial history

Received 25 February 2019; accepted 5 March 2019.


Copyright © 2019, Payal Arora and Hallam Stevens. All Rights Reserved.

Data-driven models of governance across borders: Datafication from the local to the global
by Payal Arora and Hallam Stevens.
First Monday, Volume 24, Number 4 - 1 April 2019
https://firstmonday.org/ojs/index.php/fm/article/view/9831/7743
doi: http://dx.doi.org/10.5210/fm.v24i4.9831





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