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 date: 01 September 2023
• Author: Sharon Zanti
• Date: 01 September 2023
• DOI: http://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 one’s 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,

Brayne’s 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 program’sdatause,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—

Brayne’s 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 down”and

“use data to direct nonpunitive interventions”(p. 141–145).

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;83:1423–1429. wileyonlinelibrary.com/journal/puar © 2023 American Society for Public Administration. 1423