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CALIFORNIA SOCIOLOGY FORUM VOL. 6 

Technology and Culture: How Predictive Policing Harmfully 
Profiles Marginalized People Groups 

Taryn Bates 
Department of Sociology 

 California State University, Los Angeles 
 
INTRODUCTION 

American law enforcement departments use predictive 
policing technology to predetermine crime zones within assigned 
jurisdictions. Predictive police technology is susceptible to human 
error, due to both the directives from algorithm programmers and 
the lack of vetting for bias in data collection processes. A study by 
Selbst (2017) on potential applications of predictive policing 
found police data excessively links crime to neighborhoods with 
high percentages of demographics and up of primarily non-white 
residents living below poverty income thresholds. Data mining 
systems work by gathering information from records of human 
decisions without considering the intent behind the actions; this 
creates the possibility of generating or worsening discriminatory 
outcomes. For programs based off of flawed data (2017), 
erroneous predictions reinforce existing prejudices held by law 
enforcement. Since past crime data is integrated into predictive 
policing systems, it is inevitable results will include historic biases 
within recorded enforcements. This correlation maintains 
discriminatory policing instead of creating a future of equality to 
all humankind. If predictive policing technology 
disproportionately profiles marginalized people groups, then the 
algorithms are inherently discriminatory while reinforcing 
implicit bias held by operators or enforcers of results generated by 
the program. Therefore, predictive police technology algorithms 
need to be correctly updated to avoid perpetuating harm through 
biased association of crime with minority cultures. 
 
LITERATURE REVIEW AND CONTEXT 
Implicit Bias 

In the article Bias in, Bias Out, Sandra G. Mayson (2019: 
2218) relays that “ample and mounting evidence has documented 
otherwise inexplicable racial disparities in policing, charging, 
pretrial detention, and sentencing.” Mayson (2019:2278-2279) 



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additionally notes, “two recent studies of risk assessment in action 
have argued that it was the exercise of human discretion in 
responding to risk-assessment scores that injected racial disparity 
in outcomes, rather than the risk-assessment scores themselves.” 
Mayson is supported by the data from previous iterations of 
racially stratified inequities in American history – such as the 
criminal justice practice of subjective prediction – distorting 
calculations made by predictive technology meant to advance 
objective enforcement. Records note that elevated percentages of 
minority groups in the criminal justice system is a causation of 
human prejudice provoking action when presented with neutral 
situations. Predictive policing technology decoding data from 
these conflicts could misidentify discriminatory practices of 
officers as correlations to inherent behavior within ethnicities 
subjected to high detention rates. Racial stratification through 
implicit bias is pervasive in police officers, due to the unconscious 
acceptance of societal stereotypes. Spencer et al. (2016:50) found 
that implicit bias maintains stereotypes associating people with 
darker skin tones to aggressive personality traits or criminal 
activity. This association shapes the standards of probable cause. 
As note by Glenn (2023:60), “a CBS news publication identified 
that police have killed at least one Black man or woman every 
week in 2020, as of August 31, 2020.” It feels noteworthy that 24 
of those weeks took place during COVID-19 pandemic, which 
instigated a global lockdown mid-March 2020; even under 
quarantine, Black Americans were not able to peacefully navigate 
the U.S.A. without threat to their life at the hands of law 
enforcement. This implicit bias by human police seems to place 
more support towards upgrading law enforcement with 
technological algorithms. However, those algorithms are typically 
fed data taken from the instances that were informed by implicit 
bias. 
 
Peacekeeping Robots 

Law enforcement agencies that have invested in 
peacekeeping robots can experience issues with the artificial 
intelligence programming. Artificial intelligence applications 
utilized by police agencies are susceptible to biased algorithms 
seen in predictive policing technology. Howard and Borenstein 



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(2018) clarify that robots can acquire biases through machine 
learning, despite theories that robots should be able to avoid 
prejudice. In facial recognition, choices for photo databases are 
often influenced by programmers setting outcomes to match 
unconscious beliefs or showing favoritism toward alleged experts 
without verifying credentials. This shows how AI is confined 
within parameters set by developers, causing the understanding of 
robots to mirror their creators. The model of systematic analysis 
is crucial to determining ways to diminish how bias impacts the 
implementation of peacekeeping. The use of robots by police is 
already causing harm to vulnerable people groups. Howard and 
Borenstein disclose an incident in Dallas from 2016 where a 
police robot took the life of a Black man. The researchers 
emphasize this tragedy by noting it increases pressure in strain 
from the extreme death rates of Black Americans at the hands of 
human police officers. The artificial emulation of police brutality 
creates a situation where “it is disconcerting that robot 
peacekeepers, including police and military robots, will, at some 
point, be given increased freedom to decide whether to take a 
human life, especially if problems related to bias have not been 
resolved” (2018:1527-1528). Additional mechanisms need to be 
implemented in robots tasked with keeping the peace, to eliminate 
the ability to kill humans. Robot patrols process massive amounts 
of data when patrolling urban areas; programmers trying to 
maximize protection of civilians could unintentionally facilitate 
racism in predictive policing by creating shortcuts with algorithms 
that mimic the group profiling practices of human officers. Police 
agencies considering AI as a path to unbiased patrolling should 
acknowledge precedents set by human officers can influence the 
learning processes of autonomous peacekeeping robots. 
 
Surveillance State 

Institutionalized racism in law enforcement has led to 
harmful operation of technologies in ways that reinforce implicit 
bias. Surveillance technology is unduly used by American state 
agents against marginalized people groups. Black Americans have 
specifically endured oppression by an array of military and 
policing surveillance technologies gathering information on their 
activities (Hatch 2017:125). This over regulation leads to the 



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constant harassment of Black citizens. In addition to infrared 
cameras used aerially by the FBI, social media has become a way 
for security organizations to persecute protest movements led by 
Black activists (2017:130). In a 1988 study, G.T. Marx details that  

this ‘new surveillance’ does not require the 
execution of a crime; uses covert practices, has 
redrawn the relationship between the individual 
and the state vis-à-vis the state’s and individuals’ 
rights, incorporates new technological 
developments in size and type of audio and video 
equipment available, incorporates preventive 
operations, allows for disruption and infiltration, 
can operate without a specific target/subject, and 
allows for the sharing intelligence between 
agencies. (Byfield 2019:99) 

Over the last three decades, advancements in smart technology 
through ubiquitous computing have made the use of 
reconnaissance devices even harder for average citizens to detect. 
 
Armed Drones 

Police have a history of deadly force against marginalized 
people groups through the use of drones and bombs. The article 
New Technologies of Resistance by Anthony Hatch (2017) records 
an incident where a two-pound bomb was launched by officers 
over a house in Philadelphia on May 13, 1985. Hatch (2017:129) 
describes that the “assault, which killed eleven civilians, including 
five children, and the ensuing fire that displaced hundreds of local 
residents, marked another horrific moment when state-sanctioned 
police violence against Black people and Black communities was 
represented on live television.” The bombing was a tactical 
decision to destroy the Black alliance named MOVE which was 
home-based in the area. The leader was a Black man named John 
Africa who advocated for a revolution to end capitalism and 
oppressive politics. The eventual drone assault was predicated by 
a police raid in August 1978, which included the demise of a 
Philadelphia officer. The subsequent seven years included 
constant persecution by the local police against the MOVE 
organization. The incidence in a supposed post-Civil Rights 
America emphasizes the systemic nature of institutionalized 



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racism persistence in subjugating Black citizens. Thirty years 
later, in 2015, North Dakota passed a law allowing police to use 
armed drone technology (Howard and Borenstein 2018:1528). 
The legality of deadly force in modern society permits threat of 
assault against citizens. This form of intimidation specifically 
reinforces attempts to subdue people of color. 
 
Broken Window “Community” Policing 

Predictive policing technology can be used to enforce 
policies of racially biased community policing. Implicit bias is 
inherent when enforcing community policing strategies, such as 
broken window policing which disproportionately targets 
minority groups, since it originated from racist theories. Broken 
window policies and over-maintenance policing focus on minor 
instances; a broken window is linked to the hypothesis that 
cultural atmosphere encourages misconduct (Seigel 2017:481). 
These policies create busy work by curbing trivial activities and 
consequently increase rates of racial profiling. Phillip J. 
Thompson’s article, Broken Policing: The Origins of the ‘Broken 
Windows’ Policy (2015), identifies the source for broken window 
policy in conservative theories made by Edward Banfield during 
the post-Civil Rights era. Banfield’s policy prescriptions for racial 
imbalances (2015:44) ignored societal constructions and claimed 
irresponsible behaviors were hereditary within cultural or 
biological people groups. These assertions mirror principles of 
eugenics, focusing on biology and breeding to explain perceived 
behaviors that differ from social expectations of desirable traits. 
The term community policing is a misnomer that creates division, 
due to a lack of consideration of the humanity for the marginalized 
people groups. In New York City (Byfield 2019:99), crime rates 
increased on every level and police shootings of Black men also 
became frequent instances upon implementing community 
policing.  

 
Stop and Frisk 

Racially biased policies, like stop and frisk, reinforce 
prejudiced data being presented to predictive policing technology 
by establishing past practices. The societal construct of white 
dominance in America leads to concepts of  



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racialized surveillance as an organic function of 
the racial state that is deployed via legislation, 
social practices, and technologies that use the 
‘white gaze’ to maintain the state as a ‘white 
racial space’ that constructs and reproduces racial 
categories and hierarchies. These laws, practices, 
and technologies create existential norms, thus 
violations of these norms represent existential 
crimes in a white racial state. (Byfield 2019:96) 

The sole championing of white perspective sullies the ability for 
law enforcement officers to grow cultural understanding when 
interacting with diversity among citizens. This paradigm was 
heightened after the 2001 events of 9/11 increasing attention on 
national security from terrorism. The foundation for advanced 
technologies of predictive policing lies in NYPD’s new 
surveillance implementation of stop and frisk. Even when not 
charged with a crime, the identification information obtained in 
these interactions is stored in the police databases. Black and 
Latinx young men are the largest demographic of victims stopped 
and frisked in NYC (2019:100), with almost 90 percent proven 
innocent of police allegations. The precedent for the officers 
targeting young men of color in stop and frisk endeavors creates a 
pattern in recorded data that will be integrated into predictive 
technology. 
 
Advocates for A.I. 

The opposite side of this issues argues that predictive 
police technology is a neutral method to avoid unconscious bias 
in humans; however, this is incorrect because technology is not 
inherently neutral, since programs for predictive policing mine 
human data throughout internet platforms without regard for 
accuracy. Police departments looking to avoid bias in crime 
identification can purchase predictive policing technology to 
extrapolate neutral data to improve patrolling techniques. The 
ability of predictive technology to mine data is promoted as an 
option to diminish inequality (Selbst 2017:148) because 
“unconscious bias is well enough understood that the use of 
seemingly neutral technology to take the decisions out of human 
hands is seen as a good thing.” Acknowledgement over the need 



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for better implementation of justice has led to a rise in 
contemporary smart technology focused on predictive policing. 
Northpointe, the owner of algorithmic predictive policing tool 
COMPAS, focuses on metrics of equivalence (Mayson 
2018:2233) by demonstrating both Black and white perpetrators 
who are classified as high risk by the algorithm held equal rates of 
being rearrested. These ratios imply that the algorithm is free of 
racial bias. CompStat, a mapping system that tracks data from 
large police jurisdictions (Byfield 2019:101), is the base program 
for policing maintenance which makes resource deployment 
determinations. Technological developments bolster the theory in 
law enforcement bureaus that human bias can be eliminated by 
using established criminality calculations to prevent delinquency. 
The emphasis on scientific processes contrasts the emotional 
inclination of humanity that can obscure judgements elicited from 
intense situations in American policing. 

 
A.I. Algorithms 

Predictive policing algorithms are derived from data 
mining which collects human interactions on internet platforms, 
without double-checking the accuracy of the trends. Interactions 
on the internet, especially the phenomenon of trending, operate 
out of sensationalism instead of vetted information. Impartial 
predictive policing is a marketing ploy rather than an accurate 
designation. This is noted in the article Disparate Impact in Big 
Data Policing: 

Predictive policing promises both to provide 
auditable methods that will prevent invidious 
intentional discrimination and to mitigate the 
unconscious biases attending police officers’ 
daily choices. But at the moment, such a promise 
amounts to little more than a useful sales tactic. 
Data mining is likely to introduce new 
discrimination or to reproduce and exacerbate the 
existing discrimination in society due to various 
design choices that are necessary to any data 
mining system. Risk assessment scores used in 
criminal sentencing overestimate black 



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recidivism and underestimate white recidivism. 
Selbst (2017:120) 

The imbalance of the current man-made model for risk assessment 
determination will cause the data history to skew the conclusions 
of predictive policing algorithms. Results (2017:147) from 
unconscious bias tests have proven that officers are unable to 
distinguish personal beliefs on race, culture, identity, and 
delinquency from probable cause. Even if the predictive 
technology is more neutral in data translation than of conclusion 
drawn human research, the chances are high that officers can 
misapply the results of the predictive technology due to human 
predisposition of implicit bias. To the other extreme, the 
application of CompStat changed the performance evaluation of 
officers to total crime bookings, while lowering the ability to 
implement personal discretion. Quelling judgement to standardize 
the execution of the law can easily be used to ignore grievances 
over biases affecting individuals (Byfield 2019:101). 
Homogeneity undermines the ability of officials to offer 
understanding or make exceptions when situations involve 
vulnerable individuals. 
 
DISCUSSION 

Predictive technology needs double-blind vetting in early 
stages of development to test accuracy before police 
implementation. Selbst has created the idea of “algorithmic 
impact statements” for such programs (2017:110). The statements 
would clarify the actual purpose and application of such 
technologies. He proposes (2017:119) the “regulation would 
mandate that, before adopting the new technology, police consider 
and publicly detail the predicted efficacy of and disparate impact 
resulting from their choice of technology and all reasonable 
alternatives.” The lack of field expertise to determine long-term 
consequences of using predictive police technology is a persistent 
dilemma. A metered implementation of the programs would allow 
time to catch concerns that arise. The article Black Spaces/White 
Spaces: Black Lives, Leisure, and Life Politics (Pinckney, et al. 
2018:283) gives examples of racial profiling, such as local police 
departments in New York City using the stop and frisk program to 
target Black men or Arizona’s strict immigration policy impacting 



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Latin Americans. The article goes on to detail the symbiotic 
relationship between technology and racial prejudices, where each 
entity significantly informs the rate of recurrence in cultural 
acceptance. The growth of mobile video streaming devices has 
increased the intrusions Black Americans endure through publicly 
filmed violence and killings against their ethnic group by officers. 
The perpetuation of harm is aggravated further by the reality that 
such culprits are rarely prosecuted. Media coverage raises 
awareness of homicides by persistent racism but has not seemed 
to deter recurrence. Unfortunately, since the murder of teenaged 
Emmet Till in the mid-twentieth century, the list of innocent 
victims has grown. A movement called “Say Their Names” seeks 
to humanize lives taken suddenly by actions of over policing due 
to the unfair association of Blackness to criminal behavior. This 
underscores the seriousness of challenging unconscious biases 
hidden within districts disproportionately enforcing corporal 
punishment upon minority groups.  

 
CONCLUSION 

Algorithms in predictive policing technology are based 
off of patterns; institutionalized racism in law enforcement 
reproduces problems of racial stratification. If implicit bias in 
officers continues to go unaddressed, the examples from 
enforcement of probable cause based in prejudiced beliefs will 
then continue to influence the programming and use of police 
technology. Therefore, completely reforming policing practices 
and technologies will ensure proper implementation of equity 
within law enforcement. Law enforcement resources should invest 
in developing programs to identify implicit bias in patrolling 
practices to eliminate all forms of targeted persecution against 
marginalized people groups. As noted by Bates and Glenn 
(2023:123), cultural understanding is crucial to tearing down 
systemic institutions that keep humanity subjugated under an “us 
vs. them” perception. Police brutality will continue to occur, even 
with predictive technology, as long as the root informing implicit 
bias maintains an inhumane framing of historically marginalized 
identities. 
 
 



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REFERENCES 
Bates, Taryn, and Lami J. Glenn. 2023. “Book Review: Divide & Conquer: Race, Gangs, 

Identity, and Conflict (Studies in Transgression) by Robert D. Weide.” 
California Sociological Forum Student Journal 5:120-123. 

Byfield, Natalie P. 2019. “Race Science and Surveillance: Police as the New Race 
Scientists.” Social Identities 25(1):91–106.  

Glenn, Lami J. 2023. “Introspection: An Expository Analysis of Police Brutality Against 
Unarmed Black Men.” California Sociological Forum Student Journal 5:63-
75. 

Hatch, Anthony Ryan. 2017. “New Technologies of Resistance.” Radical History Review 
2017(127):125–132. 

Howard, Ayanna, and Jason Borenstein. 2018. “The Ugly Truth About Ourselves and Our 
Robot Creations: The Problem of Bias and Social Inequity.” Science & 
Engineering Ethics 24(5):1521–1536. 

Mayson, Sandra G. 2019. “Bias In, Bias Out.” Yale Law Journal 128(8):2218–2300. 
Pinckney, Harrison P., Rasul A. Mowatt, Corliss Outley, Aishia Brown, Myron F. Floyd, 

and Katrina L. Black. 2018. “Black Spaces/White Spaces: Black Lives, Leisure, 
and Life Politics.” Leisure Sciences 40(4):267–287. 

Seigel, Micol. 2017. “The Dilemma of ‘Racial Profiling’: An Abolitionist Police 
History.” Contemporary Justice Review 20(4): 474–490. 

Selbst, Andrew D. 2017. “Disparate Impact in Big Data Policing.” Georgia Law Review 
52(1):109–195. 

Spencer, Katherine B., Amanda K. Charbonneau, and Jack Glaser. 2016. “Implicit Bias and 
Policing.” Social & Personality Psychology Compass 10(1): 50–63. 

Thompson, J. Phillip. 2015. “Broken Policing: The Origins of the ‘Broken Windows’ 
Policy.” New Labor Forum (Sage Publications Inc.) 24(2):42–47.  

 
 
 
Taryn Bates (She/Her) graduates with her Sociology B.A. in May 2024 from 
Cal State LA. Taryn is an avid writer exploring convergence of her personal 
interests with research projects on systemic racism, abolitionist and anarchist 
modalities for a changed community-focused future, cultural appreciation 
vs. cultural appropriation, cross-cultural intersections of social partner 
dances, and societal constructions of purity culture conflating sensuality to 
sexuality. She is honored to have had publications for poetry and reviews in 
CSF Volume 5 and is grateful for the inclusion in Volume 6 with a research 
article, theoretical framework, and collection of poetry. 

 


	Technology and Culture: How Predictive Policing Harmfully Profiles Marginalized People Groups - Taryn Bates



