Mapping Emerging Data Justice Challenges

Essay #3 in the Data and Pandemic Politics series on data justice and COVID-19

Editors’ Note: Drawing on the open-access collection, in this post Silvia Masiero identifies three themes for further research to address: how the pandemic is influencing and (re)shaping the digital systems related to social protection, how it is producing or amplifying data-induced injustices, and what constitutes the intersection of data injustices with pre-existing forms of oppression and inequality.

The Data Justice and COVID-19: Global Perspectives edited collection explores the data justice implications of the new, hybrid technological arrangements emerging in response to COVID-19. The question of “what COVID-19 responses mean for global data justice” is answered through nine commentaries and 33 country dispatches, illuminating common themes including public-private arrangements, intersections of technology with power abuse, redistributional effects and problems – such as data protection and human invisibilities – which remain largely unaddressed in national responses. The book’s dispatches and commentaries answer important questions on the data governance regimes induced by the pandemic, with states of emergency being declared by national governments around the world.

At the same time, the book opens up a range of new questions that, as researchers of data justice in parts of the world affected by COVID-19, we are called on to address. In an attempt to stylise a map of ‘open problems’ emerging from the book’s narratives, this short piece identifies three themes for further debate and research. Impinging on the country cases narrated in the collection, the themes relate to digital social protection, data-induced injustices, and the intersection of data injustices with pre-existing forms of oppression and inequality.

Digital Social Protection

Social protection is taking a global digital turn, purportedly aimed at improving the targeting of anti-poverty schemes through digital and often biometric measures. Before COVID-19, research had revealed a trade-off in digital social protection, for which biometric authentication of users improves targeting at the cost of greater exclusions of entitled users. The rationale for such a trade-off has been questioned, as well as the ability of biometric systems to guarantee accurate targeting. For example, work on India’s Aadhaar-enabled Public Distribution System has questioned the view that biometrics lead to greater effectiveness, because the system can record successful disbursement of rations even when these are not provided to users as per eligibility.

COVID-19 has arguably worsened the consequences of such a trade-off. Already problematic before the emergency, the cost of biometrically-induced exclusions has become more serious: with the paralysis of economic activities for vulnerable groups (such as daily-wage workers from the informal sector), the pandemic has created new poor that require social protection measures. Narratives from countries including IndiaColombia and Peru have shown the limits of biometrically-controlled social protection, especially where pre-existing infrastructures of narrow targeting have limited the ability to assist the new poor. In cases such as India, where a large migration crisis problematised a geographically-tied social protection system, measures such as the suspension of Aadhaar identification in ration shops have become an integral part of the response across states. Faced with a plethora of new poor and new access constraints for the already vulnerable, social protection systems have found themselves in a crisis that biometric technologies are not designed to face.

Data-Induced Injustices

A form of injustice emerging in the datafied world is, arguably, data-induced—a term for injustices that have to do with data, but do not stem directly from them. An example lies in the injustices endured by the “invisibles” of the COVID-19 pandemic, meaning the many populations that, being non-countable due to vulnerabilities stemming from migration, poverty or lack of legal recognition, are not contemplated by social assistance measures for the emergency. Rather than data injustices in the direct sense, which emerge from how people are seen and represented through data, these injustices are produced in a datafied world, but through indirect mechanisms that escape data-based visualising, treatment and representation.

The dispatches in Data Justice and COVID-19 contemplate many cases of data-induced injustice. Paradigmatic is that of Jordan, analysing the exclusion from digital schooling of kids whose socioeconomic condition prevents access to basic technologies. Similar narratives emerge in Brazil, whose social assistance system largely escapes the new poverties produced by the pandemic. In India, a contact tracing app (Aarogya Setu, literally a “bridge to wellness”) offers the (disputed) health protection of contact tracing only to smartphone owners, estimated to be 36.7% of the total population. With COVID-19, data-induced injustices emerge as a further device to explore the consequences of the crisis.

Intersecting Injustices

It has been observed that data injustices often build on pre-existing forms of injustice and inequality, which technology contributes to reinforce and crystallise. Examples of algorithmic processes impinging on race and gender bias are cases in point, illuminating the intersection of data injustices with extant forms of inequity and oppression. In such examples, data constitute a component of structural asymmetries that is further reified by technological regimes.

Along the same line, the injustices detailed in the book overlap with previously established forms of oppression that COVID-19 responses bring to life. This applies to the Indigenous populations of North America narrated by Duarte in Data Justice and COVID-19, whose leaders are excluded from decision-making around the Coronavirus Aid, Relief, and Economic Security (CARES) Act and technology response. In Hungary, direct connections appear between state of emergency and consolidation of authoritarian power through data gathering and sharing, with cases such as Uganda and the Philippines echoing such dynamics of power abuse. In the same remit are the multiple cases, elsewhere referred to as the hybrids of COVID-19, in which government and private companies have given life to profit-generating surveillance technologies with unclear implications for the privacy and data protection of users. In the scenarios depicted by the book, data injustice overlaps with injustices in the material world, inviting systemic enquiries on their mutual intersections.

Conclusion: A Map of New Themes

As Sean McDonald notes in his commentary in Data Justice and COVID-19, the collection is hard to read. It is so especially as it documents, dispatch after dispatch, abuses of political and technological power blended together in the context of a global pandemic. But while being so, it is a book that carries a unique theoretical and practical contribution at this historical time. From a theoretical perspective, it is a book that illuminates the value of data justice as a theoretical device to shed light on the consequences of public-private hybrids operating in emergencies. From a practical one, published just months after the declaration of a global pandemic, the book offers a multifaceted vision of the data justice implications of its immediate aftermath.

It is in light of the book’s contribution that the open problems highlighted here call for research in a global context affected by the impacts of COVID-19. This mapping is stylised, and more nuances exist in the histories of the peoples and countries facing the global emergency. Still, as hybrid architectures of data governance diffuse worldwide in response to the pandemic, the themes proposed here may offer research directions to further develop a data justice agenda at this critical time.

Silvia Masiero is an Associate Professor of Information Systems at the University of Oslo, Department of Informatics. Her research focuses on the use of Information and Communication Technologies (ICTs) in the field of socio-economic development. She studies the multiple forms of embeddedness of the ICT artefact in development policy and governance, with a specific interest in its participation in the politics of anti-poverty programmes.

Suggested citation: Masiero, S. (2020, November 20). Mapping Emerging Data Justice Challenges. Data and Pandemic Politics, 3.

About the project

Places and populations that were previously digitally invisible are now part of a ‘data revolution’ that is being hailed as a transformative tool for human and economic development. Yet this unprecedented expansion of the power to digitally monitor, sort, and intervene is not well connected to the idea of social justice, nor is there a clear concept of how broader access to the benefits of data technologies can be achieved without amplifying misrepresentation, discrimination, and power asymmetries.

We therefore need a new framework for data justice integrating data privacy, non-discrimination, and non-use of data technologies into the same framework as positive freedoms such as representation and access to data. This project will research the lived experience of data technologies in high- and low-income countries worldwide, seeking to understand people’s basic needs with regard to these technologies. We will also seek the perspectives of civil society organisations, technology companies, and policymakers.