Algorithmic risk governance: Big data analytics, race and information activism in criminal justice debates
| Author | Kelly Hannah-Moffat |
| DOI | 10.1177/1362480618763582 |
| Published date | 01 November 2019 |
| Date | 01 November 2019 |
| Subject Matter | Articles |
763582TCR0010.1177/1362480618763582Theoretical CriminologyHannah-Moffat
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Article
Theoretical Criminology
2019, Vol. 23(4) 453 –470
Algorithmic risk governance:
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Big data analytics, race and
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information activism in
criminal justice debates
Kelly Hannah-Moffat
University of Toronto, Ontario, Canada
Abstract
Meanings of risk in criminal justice assessment continue to evolve, making it critical
to understand how particular compositions of risk are mediated, resisted and re-
configured by experts and practitioners. Criminal justice organizations are working with
computer scientists, software engineers and private companies that are skilled in big
data analytics to produce new ways of thinking about and managing risk. Little is known,
however, about how criminal justice systems, social justice organizations and individuals
are shaping, challenging and redefining conventional actuarial risk episteme(s) through
the use of big data technologies. The use of such analytics is shifting organizational risk
practices, challenging social science methods of assessing risk, producing new knowledge
about risk and consequently new forms of algorithmic governance. This article explores
how big data reconfigure risk by producing a new form of algorithmic risk—a form of
risk which is posited as different from the social science (psychologically) informed risk
techniques already in use in many justice sectors. It also shows that new experts are
entering the risk game, including technologists who make data public and accessible to
a range of stakeholders. Finally, it demonstrates that big data analytics can be used to
produce forms of usable knowledge that constitute types of ‘information activism’. This
form of activism produces alternative risk narratives, which are focused on ‘criminogenic
structures’ or ‘criminogenic policy’.
Keywords
Actuarial risk assessment, big data, predictive police algorithms, race, sentencing
Corresponding author:
Kelly Hannah-Moffat, University of Toronto, 63 Banff Rd, Toronto, Ontario, M4S2V6, Canada.
Email: hannah.moffat@utoronto.ca
454
Theoretical Criminology 23(4)
Introduction
Some argue that we are now living in an ‘algorithmic age where mathematics and com-
puter science are coming together in powerful new ways’ that influence individual
behaviour and governance (Danaher et al., 2017: 1). Scholars in various fields are
examining how big data is being assembled and used. For example, international secu-
rity agencies access and sort through extensive communications metadata to define,
identify and neutralize national security risks, including terrorism (Lyon, 2014).
Governments also routinely merge financial transactional data with various forms of
client case file data to detect and respond to fraud (Ruppert, 2012), and health data are
mined and tracked to predict and monitor disease and prevent outbreaks (Thomas,
2014). Policing agencies are becoming increasingly sophisticated in their uses of bio-
data, facial recognition software, traffic cameras, body/car cameras, licence plate read-
ers and Global Positionning System (GPS) locators, all of which produce digital data
that can be combined to identify and track individuals in the pursuit of safety and secu-
rity (Brayne, 2017; Gates, 2011; Gitlin, 2012; Kitchin, 2014, Lupton, 2015; Smith and
O’Malley, 2017).
Several private companies are marketing and distributing data-driven technologies to
criminal justice institutions (Brayne, 2017). One of the best known is PredPol, a predic-
tive policing software: its software developers work with local police agencies to map
criminal events in real-time, allowing police to efficiently deploy resources (PredPol,
2017).1 Florida uses machine learning algorithms to set bail amounts (Eckhouse, 2017).2
Some US prisons are also adopting new technologies: for example, some provide prison-
ers with tablets pre-loaded with approved applications to manage their mail and music
(Tynan, 2016). While these technological enhancements in prisons can be seen as pro-
gressive, they also reflect a trend towards digital monitoring and decision making. These
tablets allow for the collection, collation, manipulation and storage of vast amounts of
data about individual prisoners, their families, including forms of data that are unrelated
to prison (e.g. Facebook, credit histories, internet activity, health records, neighbourhood
information). Digital storage and mining of this type of data is quite appealing to penal
officials and others.
Although big data is often acclaimed for its ability to enrich our understanding of
particular phenomena (Lazer et al., 2009), it is also ‘a political process involving ques-
tions of power, transparency and surveillance’ (Tufekci, 2014: 1). It is not as ‘objective,
neutral, or complete as they are portrayed in mainstream media representations’ (Lupton,
2015: 101). Instead, big data, its incumbent analytics and the knowledges they produce
are socio-political and cultural artefacts that are transforming how we live, work and
think about social problems (Lupton, 2015). Its recent uptake by criminal justice actors
in relation to the production of risk requires analysis. Inasmuch as criminal justice organ-
izations are now embracing ‘big data’ analytics (Brayne, 2017), they are engaged in vari-
ous new forms of ‘algorithmic governance’ (Danaher et al., 2017). Big data has
invigorated the criminal justice system’s (CJS) focus on ‘smart’, ‘data-driven’ and ‘evi-
dence-based’ solutions, allowing individuals and private companies with technical
expertise to offer various big data informed options for criminal justice actors and organ-
izations. CJS representatives are working with computer scientists, software engineers
Hannah-Moffat
455
and private companies that are skilled in data analytics to produce new ways of thinking
about and managing risk. These collaborations are expected to enhance the efficiency of
prediction systems, while at the same time designing them in a way that will not operate
in a discriminatory manner. However, little is known about how CJS, social justice
organizations and individuals are producing, challenging or redefining conventional risk
episteme(s) through the use of big data analytics (Kitchin, 2017). As such, the introduc-
tion of big data technologies warrants analysis both because the concept of risk is central
to our legal and criminal justice culture, and because it represents an epistemic deviation
from risk assessments that are grounded in psychological disciplines.
The governance of crime and understandings of risk continues to be fluid and shifting
(Maurutto and Hannah-Moffat, 2006). For several decades, CJS have actively embraced
a variety of risk logics and technologies with the goal of enhancing efficiency, account-
ability and equity. Systematic and psychologically informed actuarially based risk
assessments are widely accepted as being rooted in an evidence base, and by extension
are considered to be more scientifically credible than professional discretion and clinical
judgement in assessing risk (Skeem and Lowenkamp, 2016). Criminologists and soci-
ologists have studied how risk logics have become embedded in criminal justice plan-
ning and practice, and how psychologically informed actuarial risk analyses are used to
predict and prevent crime, enhance security, sentence offenders and manage penal popu-
lations (Feeley and Simon, 1992; Hannah-Moffat, 2013; Harcourt, 2007; Kemshall,
2003; O’Malley, 1992, 2004, 2010). This research has helped clarify how knowledge and
understandings of crime are framed and addressed though risk logics. Scholars have also
argued that risk informed practices of governing crime have led to the over-representa-
tion and over-policing of particular segments of the population, mainly racialized indi-
viduals (Chouldechova, 2017). Researchers have also turned their attention to the ways
in which newer big data technologies aid in predicting risk levels, managing the ‘crime
problem’ and solving these inequalities (Brayne, 2017; Eubanks, 2018; Ferguson, 2017;
Smith and O’Malley, 2017).
This shift to big data analytics represents a notable departure from other algorithmi-
cally influenced risk technologies. Despite the fact that social scientists have long used
large official data sets to map crime patterns and track offenders, the advent of big data
analytics is considered to be a game changer for criminal justice governance because of
its phenomenal speed, breadth and depth capacities. It also entails new forms of data
from an assemblage of sources, including but not limited to smart phones, digital cam-
eras, GPS tracking devices, internet searches, consumer databases, social media, open
data sources and smart software (Lupton, 2015; Smith and O’Malley, 2017). The term
‘big data’ then generally refers to a wide array of digitally stored information about indi-
viduals, organizations, companies and events. The term can also be used to describe the
techniques used to efficiently assemble and disassemble this information for a variety of
commercial and non-commercial purposes. Still, we have little understanding of where
‘big data’ actually comes from, how it is used or how it lends authority and justifies deci-
sions. As a result, ‘big data has...
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