[Commlist] CFP - Foreseeing Race: The Technology and Culture of Risk Prediction after the Datalogical Turn

*Foreseeing Race: The Technology and Culture of Risk Prediction after 
the Datalogical Turn *

Special issue, Journal of American Studies 
<https://www.cambridge.org/core/journals/journal-of-american-studies>

Editors:

Georgiana Banita (University of Bamberg)

R. Joshua Scannell (The New School)

The current crisis of legitimacy in Big Tech and AI provides an opening 
for forceful scholarly criticism of predictive and surveillant 
technologies’ impacts on social life. The erosion of the supposed 
trustworthiness and beneficence of the industry as a whole, and the Big 
Four (Apple, Google, Facebook, Amazon) in particular, has precipitated a 
discursive disillusionment with tech. And, while this has raised 
questions of technological racism in certain segments, the broader role 
that race plays in driving the bad actions of the tech industry remains 
underreported and undertheorized. Perhaps more importantly, there is a 
real opportunity here to imagine forms of struggle and liberation that 
/may/ arise through tech, but certainly will not do so by themselves. In 
other words, by focusing on racialized technology in an American 
context, we hope to intervene in assumptions that the answer to these 
forms of racism is merely “better” technology, or instead depends on a 
rethinking of the terms of the discussion.

To name one controversial example: Big Data surveillance and risk 
prediction have recently gained prominence within public safety and law 
enforcement circles. Algorithmic decision-aids, which encode 
criminological theories about crime rates and process vast amounts of 
data into patterns and trends, have applications that range across 
several fields from sentencing and the anticipation of recidivism to 
predictive policing. Even though it remains unclear to what extent 
predictive crime analytics systems absorb human biases, the technology 
is already widespread in the US, UK, and continental Europe. Recent work 
in sociology, political science, and computational social sciences as 
well as high-profile human rights investigations have launched a 
much-needed critical narrative by demonstrating that predictive 
algorithms undermine equal justice and preclude individualized 
assessments. While literary and cultural studies have been slower to 
recognize the amplifying effect of unfair algorithms on existing 
disparities of race, their methods of diachronic analysis and increasing 
societal engagement have much to offer in answering key questions. How 
did predictive technology gain social acceptance? And which historical 
strategies of racial oppression inform contemporary methods of data 
mining and crime forecasting?

In her celebrated examination of data-based inequality, Virginia Eubanks 
argues that the world is enveloped in an inscrutable network of 
“informational sentinels” that frame and allocate the distribution of 
resources and privation outside of human agency or oversight. More 
specifically with regard to race, new forms of racial profiling that 
Safiya Noble has termed “technological redlining” consolidate existing 
racial biases, inequalities, and the maldistribution of life chances 
while formally appearing to operate “race blind.” In focusing on digital 
red flags attached to people of color, these and other sociological 
studies have uncovered the significant role of data discrimination in 
the perpetuation of racism during the 21st century. Recent scholarship 
by Ruha Benjamin about what she designates “the New Jim Code” has 
additionally proven the benefits of tracking present algorithmic tools 
to past techniques of promoting racial discrimination and White supremacy.

This assemblage of techniques and technologies is designed to measure 
and manage human capacities, predict tendencies, and control bodies in 
space and time without ever conjuring a “human” referent as such. 
Instead, an array of systems generates and sifts data black boxed from 
human comprehension and abstracted from any recognizable set of human 
properties, yet output consequences of vulnerability and premature death 
to the same groups of people that are historically and systemically 
marginalized. Patricia Clough et al have called this mode of 
governmentality the “datalogical,” a sort of technical read/write 
program for racism-by-proxy that writes racial formations even as it 
reads disembodied and decontextualized “data.”

Given the long durée and infrastructural nature of racial bias in the 
US, literary and cultural methods are well-positioned to engage with the 
continuities of racial imaginaries and thereby reveal the occluded 
premises of racialized prediction in the present. Furthermore, querying 
the purported advantages of state and infrastate surveillance and 
prediction systems can revitalize cultural, ethical, and aesthetic 
debates on the uses of narrative and visual media in making sense of 
this disquieting morphological relation between individuals and 
institutions of social control. Can the toolkit of cultural studies 
reveal a clearer genealogy of digitally-driven surveillance and crime 
analytics? And might perhaps the collating, speculative nature of 
cultural interpretation itself not be entirely dissimilar from the 
practices of automated intelligence?

In navigating this terrain, our interdisciplinary special issue aligns 
contemporary American and British scholarship in the area of race and 
digital technologies with broader cultural theorizations of how race 
works as a structuring agent in the history and imaginary of the United 
States. The essays will share a commitment to political and social 
critique while adopting cultural studies approaches to explore the 
racialized dimension of Big Data surveillance; the racial inequities 
embedded in prediction technologies; and the problematic invisibility of 
algorithmic (in)justice. The special issue will thus provide a 
US-focused cultural conceptualization of predictive technology within 
the emergent fields of automation studies and the ethics of Big Data.

Potential topics include but are not limited to:

Law Enforcement

-the use of socio-politically derived Artificial Intelligence in law 
enforcement

-epistemologies and temporalities of predictive policing

-alternative frameworks for rethinking the future of race and high-tech 
policing

-aesthetic and narrative approaches to crime forecasting

-continuities between digitally-driven racialized surveillance and 
“analog” methods (e.g., Terry Stops, broken windows policing, etc.)

Surveillance

-militarized surveillance practices in formally non-conflict 
environments (especially drone surveillance)

-countermeasures to racialized Big Data surveillance (including cloaking 
and the development of alternative “black data”)

-statehood, governmentality, and the surveillance of race

-relationship of digital surveillance and prediction to broader 
extractive practices of racial capitalism

Cultural Practice

-literary, visual, and quotidian responses to surveillance and 
technologies of prediction

-historical precedents and roots of current algo-racism

-history and cultural production around the concept of prediction

-racialized prediction in the context of Afrofuturism, technology, and 
the posthuman

We are asking for abstracts of around 500 words by March 31. Article 
drafts of 8,000 to 12,000 words will be due by October 31, no article 
processing charge. Please email abstracts and queries to both editors 
at: (Georgiana.Banita /at/ uni-bamberg.de) 
<mailto:(Georgiana.Banita /at/ uni-bamberg.de)>and (ScannellRJ /at/ newschool.edu) 
<mailto:(ScannellRJ /at/ newschool.edu)>.


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