Predict and surveil: Data, discretion, and the future of policing. By Brayne S , New York, NY: Oxford University Press. 2020. pp. 288. $29.95 (hard cover). ISBN: 9780190684099

Published date01 September 2023
AuthorSharon Zanti
Date01 September 2023
DOIhttp://doi.org/10.1111/puar.13714
BOOK REVIEWS
Predict and surveil: Data,
discretion, and the future of
policing
By Brayne S, New York, NY: Oxford
University Press. 2020. pp. 288. $29.95
(hard cover). ISBN: 9780190684099
Sharon Zanti
School of Social Policy and Practice, University of Pennsylvania, Pennsylvania,
Pennsylvania, USA
Correspondence
Sharon Zanti, School of Social Policy and Practice, University of Pennsylvania,
Philadelphia, PA, USA.
Email: szanti@upenn.edu
Policing systems increasingly rely on big data and auto-
mated systems to decide who, when, and where to police
(p. 2). Software programs and algorithms that leverage big
data are often marketed and perceived as inherently objec-
tive tools for decision-making and a cure-all for structural
biases embedded in public systems. However, Brayne dispels
this notion by showing the social, subjective nature by which
policing data are collected, stored, analyzed, and deployed
in practice. She takes a clear stance that big data analytics
has yet to deliver on its promise of reducing inequities in the
policing system, based on evidence from her five-year eth-
nography of the Los Angeles Police Department (LAPD) and
a synthesis of the existing literature. Brayne conducted inter-
views, ride-alongs, observations, and archival research with
the LAPD, and also spent time off-duty with police officers,
interviewed federal and industry experts in policing and sur-
veillance, and examined the legal frameworks surrounding
big data and policing. This constellation of sources allows
Brayne to paint a nuanced picture of the micro and macro
forces influencing how big data analytics are being (mis)used
in policing. In addition, she articulates potential solutions for
this field to course correct that are relevant for any public
administrator to consider, given the proliferation of big data
analytics across government systems.
To build up to high-level solutions, Brayne first traces
the history of data collection and use in policing, paying par-
ticular attention to the increasing reliance on private firms
to provide software programs, algorithmic tools, and big
datasets. This private industry creep has allowed the LAPD
to extend surveillance beyond just those under suspicion
for criminal activity (i.e., dragnet surveillance) and to use
people- and place- based algorithms to predict crime
(i.e., directed surveillance). Dragnet and directed surveillance
allow police to retrace ones digital footprint and forecast
potential criminal hot spots at an increasingly granular level,
which is highly valuable for solving crimes. However, these
surveillance strategies are also problematic for three key rea-
sons: (1) the development and ownership of surveillance
tools within the private sphere allows for a lack of transpar-
ency and oversight that is not normally permissible in public
agency operations, (2) the underlying data these tools rely
on contain well-documented biases (e.g., police records)
and/or are being reused from other unrelated systems
without a clear purpose (e.g., bankruptcy records, vehicle
registrations, personal property records, name and address
combinations), and (3) citizens do not have the opportu-
nity to explicitly consent to or refus e their non-polici ng
data ending up in police databases. Given that private
industry creep affects government systems broadly,
Braynes observations of problematic data use within the
LAPD can be translated into relevant questions for all pub-
lic administrators to reflect upon: To what extent are pri-
vate contractors and providers being held to government
standards for transparency and oversight? What biases are
baked into data used for decision-making, from the time
data is collected and analyzed to the ways in which data-
driven responses are implemented? How can citizens
weigh in on an agency o r programsdatause,andhoware
they being protected (or not) from harmful uses of data
through governance and legal frameworks?
This leads to a key strength of the book as a whole
Braynes ability to use the specific case of the LAPD to craft a
broadly applicable narrative about data use in public sys-
tems. She offers six provocations meant to send readers into
the world ready to seek change,such as slow downand
use data to direct nonpunitive interventions(p. 141145).
These are pertinent points to wrestle with across the public
sector and among researchers, evaluators, and analysts who
rely on government-held data. Within these provocations
Brayne does not encourage public systems to wholly reject
big data analytics, but rather, to deliberately assess the biases
inherent to different datasets and the potential unintended
consequences before deploying tools. Moreover, public sys-
tems should start with a clear purpose for data use and craft
the operationalization of data around that purpose. For
example, reducing crime, cutting costs, and ensuring equita-
ble outcomes are distinct goals that necessitate distinct and
sometimes competing strategies. Brayne is not ambivalent
about the types of goals police departments should pursue,
though. She advocates that data are used to target programs
and services that help people rather than penalize them.
When applying this to a broader public administration
Received: 1 August 2023
DOI: 10.1111/puar.13714
Public Admin Rev. 2023;83:14231429. wileyonlinelibrary.com/journal/puar © 2023 American Society for Public Administration. 1423

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT