The Discomfort of Death Counts: Mourning through the Distorted Lens of Reported COVID-19 Death Data

Raji, Inioluwa Deborah

doi:10.1016/j.patter.2020.100066

Cited by 5 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, critiques of data management and distribution show the need for growing a culture of care for the subjects of datasets in machine learning, i.e., to keep in mind that ''data are people'' and behave appropriately toward the people from whom we collect data. 149 Reflections on issues of data reuse emphasize the connection between data and its context, and the risks of harm (to data subjects and others) that arise when data is disconnected from its context and carried to and recontextualized in new domains. Legal Review vulnerabilities inherent to current data collection and distribution practices in machine learning as well as the often precarious and under-compensated nature of dataset work reveal the complexities of data development and use within the context of society.…”

Section: Discussionmentioning

confidence: 99%

Data and its (dis)contents: A survey of dataset development and use in machine learning research

et al. 2021

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Data and its (dis)contents: A survey of dataset development and use in machine learning research

et al. 2021

Self Cite

View full text Add to dashboard Cite

“…To respond to faulty databases with a better, cleaner, faster, statistical feed on death, disease, and suffering is, as Raji notes, to have missed the point (Raji 2020). The purpose of this article is not to argue for better databases, nor even to argue against them.…”

Section: Discussionmentioning

confidence: 97%

The Testing Database as Pandemic Technology: Reflections on the COVID-19 Response in India

Chowdhury

Basu

2021

MAT

View full text Add to dashboard Cite

This article examines the COVID-19 response in India, viewing it as deeply enmeshed in the dynamics of the ‘database’ as an emerging technology of governmentality. Databases aim to translate entire populations into units of information abstracted from social identities and local specificities. In the context of the coronavirus pandemic, bureaucratic state systems attempt to manage and respond to the health crisis via databases collating testing data across the country. Problematising COVID-19 testing databases, we delve into the logic of database governance. We find that as a tool of governance the database falters in its attempts to compress complex identities and locations into de-contextualised units of information. As the complexity of lived reality interrupts the logic of databasing, state discourse on ‘unintended consequences’, ‘leakages’, ‘duplication’, and ‘reconciliation’ processes in the management of databases abounds and the ambivalence of databases becomes manifest in the COVID-19 response. In this article, we use secondary data to understand how testing databases intervene and interact with complex realities to establish bureaucratic order around a pandemic. We posit that COVID-19 testing databases should be understood as being embedded in emerging database governmentalities that supplant care of the population with the maintenance of databases.

show abstract

“…Taylor (2020) likewise argues that a critical COVID-19 data praxis must divest itself from a preoccupation with absences of data as merely "gaps" to be filled through the collection of more and more data. Taylor (2020) argues that such a response only further dehumanizes life (see also Raji, 2020). Instead, Taylor (2020) advocates for a feminist ethics of care to re-embody COVID-19 in the personhood of those rendered both knowable and unknown in systems of datafication.…”

mentioning

confidence: 99%

Viral Data

Leszczynski

Zook

2020

Big Data & Society

View full text Add to dashboard Cite

We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as granting objective access to truth and attendant practices of tracking, data analytics, algorithmic prediction, and data-driven targeting of individuals and communities. This collection of papers explores how data (and their absences) is figuring in the making of the discourses, lived realities, and systemic inequalities of the uneven impacts of the coronavirus pandemic.

show abstract

The Discomfort of Death Counts: Mourning through the Distorted Lens of Reported COVID-19 Death Data

Cited by 5 publications

References 2 publications

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Data and its (dis)contents: A survey of dataset development and use in machine learning research

The Testing Database as Pandemic Technology: Reflections on the COVID-19 Response in India

Viral Data

Contact Info

Product

Resources

About