Race, Again: Facial Recognition Technology 1
Race, Again: Facial Recognition Technology
Dole Smith
Liberty University
CJUS 601: Criminal Justice Program Evaluation
Dr. Sadulski
10/13/2023
Author Note
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Cristy Smith
Email: cmsmith72@liberty.edu
Race, Again: Facial Recognition Technology 2
Abstract
The government and private companies have collected data from civilians for a long time. When
recognized they find ways to justify the acts they have committed for reasons of national
security, economic stability, or other “benefits” said to help the American people. However, these
beneficial trade-offs have not affected all individuals in the same way and have been proven to
disproportionately affect communities of color more often. Facial recognition technology has
often reflected the existing social biases and this can build upon the harmful racial cycles we see
today. This paper will discuss what facial recognition technology is, examine its advantages and
disadvantages, explain the problem in greater detail, discuss possible solutions, and further
discuss what is currently being done about it.
Race, Again: Facial Recognition Technology 3
Introduction
Law enforcement officers are crucial members of the community because they help keep
communities safe and secure when done right. The combining of technology with law
enforcement has made an incredible difference in today's times. Technology has evolved with
innovations such as GPS tagging systems, body cameras, car dashboard cameras, and thermal
imaging. When software and data-sharing solutions were introduced in law enforcement, it
transformed policing by delivering reliable software and systems to law enforcement agencies
around the country, state, and locally. In today’s time, law enforcement agencies have a wide
range of technological advancements to use for day-to-day job duties.
There are many useful technologies that are used by law enforcement to help manage
data and keep the community and its people safe. The first technical advancement used by law
enforcement agencies is robotics. Robotics aids law enforcement in many ways. Robotic
technology has been particularly useful in dangerous situations where it is not safe for agents to
enter (2018). For example, bomb squads use robotic technology to dispose of suspicious
packages, get a closer look at bombs, or do other high-risk tasks deemed too risky for humans
(2018).
Another technical advancement used by law enforcement agencies is electronic
monitoring systems. An individual who has been prosecuted for a minor offense has to use what
is known as an electronic monitoring system (2018). This system allows the individual to receive
a suitable punishment, while also remaining at home or going to work (2018). It has been known
to be used for individuals on house arrest for years and now the technology has made a drastic
change throughout recent years. The electronic monitoring systems we see today have a lot more
Race, Again: Facial Recognition Technology 4
advanced technology. They are able to clearly and accurately provide constant surveillance using
GPS monitoring systems, visual and audio recording, and much more (2018).
Cloud computing is another technical advancement used by law enforcement agencies.
This helps agencies store and manage large amounts of data in their computerized systems
instead of the information getting lost or burnt when it is just on paper (tech). Data such as
fingerprinting, photographs, video footage, and police reports can all be categorized and placed
into the cloud computing system (2018). This system is helpful because it assists in collecting
data and the performance of effective crime analysis (2018). With the help of cloud
computerizing, a centralized criminal justice database can be shared among law enforcement
agencies to provide more up-to-date and insightful information that could, in turn, assist in an
investigation, help decision-making, and more (2018).
Dash and body cameras have become a popular everyday tool used in law enforcement. It
was developed to better protect officers and the public they serve as evidence of what really
happened during an officer's investigation into a call (2018). The dash camera is located on each
and every police vehicle and can record incidents that can occur in front of the vehicle (2018).
Body cameras are worn by law enforcement officers to record and make a record of all incidents
and interactions that can occur while an officer is on the job in the field (2018).
There are many more technological advances law enforcement officers use on a daily
basis but there is one that has the most controversy surrounding it. It is the emerging
technological tool of face recognition software. Face recognition software is supposed to be used
to help improve safety and security in multiple instances, for example, kidnapped victims. Face
recognition works by entering a facial image into a system that automatically encodes the image
using an algorithm, compares the image to profiles already stored in the system, and results in a
Race, Again: Facial Recognition Technology 5
list of the most likely matches (Brayne & Christin, 2021). Unknown to most law enforcement
uses face recognition technology to compare a suspect’s photo to different mugshots and driver’s
license images (Najibi, 2020). The estimate for how many American adults have their photo in
a facial recognition network used by law enforcement, without their knowledge is half, which is
over 117 million individuals (Najibi, 2020). While facial recognition is thought to have a
multitude of benefits, participation in this network is completely without consent, and awareness,
and lacks the legislative oversight it desperately needs. In more disturbing news, the current use
of facial recognition technology has a significant amount of racial bias in the system that is
particularly toward Black/African Americans (Najibi, 2020).
Problem
Facial recognition technology is fairly new and is taking the world by storm by
transforming the way individuals live and work. Facial recognition is a part of the AI-powered
family and works by identifying and analyzing the specifics of an individual’s facial features
(Faraldo Cabana, 2023). Further analysis states that facial recognition technology uses face
recognition software that can identify a human face through an image or a video (Faraldo
Cabana, 2023). For example, the software can look at intricate details such as the space between
your eyes or the particular way your cheekbones curve, to compare to its database for recognition
or verification.
There are several reasons why facial recognition technology is helpful. The first way
facial recognition technology is helpful is it can find missing people or identify the perpetrators.
Facial recognition technology can use camera feeds to find missing people or identify criminals
by comparing faces with faces on a watch list for law enforcement (Kioumourtzis et al., 2021).
This technology has been used on numerous occasions to find missing children or age them
Race, Again: Facial Recognition Technology 6
using advanced software to predict what the child could look like now, using a photo that was
taken the year the child was abducted (Kioumourtzis et al., 2021). This technology can also give
live alerts to law enforcement, to help track a potential match that has been spotted on the system
in real time (Kioumourtzis et al., 2021).
Another way facial recognition technology is helpful is it can protect businesses from
theft. There have been preemptive measures for shoplifting that use facial recognition software in
many businesses. When shoplifting occurs, business owners use the software and the security
cameras to be able to identify who the suspect is In this process, the suspect’s image is
compared to a database of known thieves, and a list of potential suspects is chosen or if they are
not in the system, the thief is now cataloged in the system for future reference (Kioumourtzis et
al., 2021).
With facial recognition technology, high targeted areas such as banks and airports have
better security. Facial recognition technology can be used as a preventative security measure in
sensitive locations. Just like the system can identify criminals that pop up in businesses, the
software can also help identify criminals that pose a risk when they enter banks, airports, or even
border checks (Kioumourtzis et al., 2021).
Facial recognition technology is important when it is used by law enforcement. It is also
arguably very convenient for the average everyday person. This technology requires fewer steps
than other security measures such as password verification and fingerprint (Liu & Cheng, 2020).
Also, this technology does not require the use of any physical contact or direct human interaction
and uses AI to make the process not only automatic but smoother. The average person with a
smartphone can make cash or credit payments at stores using the facial recognition system.
When an individual points their smartphone toward their face, the AI system recognizes your
Race, Again: Facial Recognition Technology 7
face and can use your information to charge your account (Liu & Cheng, 2020). Lastly, the
system conveniently limits touchpoints that are needed to, for example, unlock a smartphone
device or perform any activity that requires a PIN code, a fingerprint, or a password.
There are many benefits to facial recognition technology that benefit not only law
enforcement but also community members. Even with all the benefits, there are still many
disadvantages of facial recognition technology. One major disadvantage of facial recognition
technology is the issue of accuracy. Although this technology is far more accurate than the
human eye, it is still not 100% accurate all the time. Facial recognition technology can struggle
to identify individuals with darker skin tones, individuals with similar facial features, and
individuals wearing masks or impersonating another person (Pashentsev, 2020). For example,
individuals who have an identical twin or wear heavy makeup that changes their natural
appearance can be hard for facial recognition technology to detect.
This leads to the next facial recognition technology disadvantage which has been the
most criticized, racial and gender bias. When the facial recognition technology system is trained
and set to an algorithm that does not represent the diversity of the population, it leads to racial
and gender bias (Pashentsev, 2020). If the software misidentifies an individual, it could lead to
false accusations and wrongful convictions.
Another disadvantage of facial recognition technology is the concern about individual
privacy. Some critics of facial recognition technology believe it violates the rights of individuals
such as being monitored without the other’s permission or knowledge (Pashentsev, 2020). Even
some companies have shared concerns about their employees possibly not wanting to work at
their establishment because they feel as though they will be watched, which can lead to morale
problems in the company and a high turnover rate (Pashentsev, 2020).
Race, Again: Facial Recognition Technology 8
Since facial recognition is not perfect software, another disadvantage is it can be tricked.
Facial recognition software relies heavily on images, if the system is set to look for someone
with specific features, for example, a beard, it can easily be tricked if a person alters their
appearance (Kyle & White, 2019). This is due to some facial recognition technology not being
high-quality enough to identify an individual accurately (Kyle & White, 2019).
The last disadvantage of facial recognition technology is its difficulties in different and
certain conditions. Facial recognition systems can have difficulties detecting individuals in a
variation of lighting, angles, and facial expressions (Purshouse & Campbell, 2019). Facial
recognition systems are trained to detect frontal images of different faces (Purshouse &
Campbell, 2019). This is a problem because realistically, individuals can turn their face to the
side, or tilt their face up or down.
The biggest problem that exists because of facial recognition technology is the
government and private companies’ long history of collecting data from civilians and justifying
the loss of complete privacy without informing citizens of this action. It also does not affect all
individuals equally but actually disproportionately affects communities of color more. Without
proper regulation, facial recognition technology can continue harmful and virtuous cycles
through surveillance patterns that have often reflected existing societal biases. Facial recognition
technology has enabled more and precise discrimination as the continuance of law enforcement
misinformed decision-making.
The history of race and surveillance in the United States of America is a long one with
the most widely known case being the FBI tracking of Martin Luther King Jr., Malcolm X, and
other civil rights activists during the civil rights movement (Bacchini & Lorusso, 2019). In more
recent years, the government and law enforcement agencies’ responses to public outcry and
Race, Again: Facial Recognition Technology 9
protests about the appalling patterns of police officers have raised concerns regarding whether
the use of surveillance techniques has been used appropriately, especially in communities of
color (Bacchini & Lorusso, 2019). In 2015 a police department in Baltimore reported using
aerial surveillance, location tracking, and facial recognition technology to further identify what
individuals were publicly protesting the disheartening death of Freddie Gray ((Bacchini &
Lorusso, 2019). In another incident, the U.S. Department of Homeland Security, or DHS,
deployed helicopters and drones to watch and survey the protest of George Floyd in fifteen cities
(Bacchini & Lorusso, 2019).
Facial recognition technology and systems only meet a few enacted legal restrictions at
the federal level (Bacchini & Lorusso, 2019). There have been over seven states and about
twenty municipalities that have been established that limit the government’s use of facial
recognition technology in certain areas (Purshouse & Campbell, 2022). For example, a law
enacted in 2021 in the state of Maine prohibits government use of facial recognition except in
certain cases such as identification of missing or deceased individuals and fraud prevention
(Purshouse & Campbell, 2022). In the same year, Minneapolis passed an ordinance that
prevented facial recognition technology from obtaining from third parties or knowingly using
any information that has been collected through the facial recognition system. There have been
many states seeing the dangers of facial recognition technology, yet there is no uniformity
throughout all of the United States involving facial recognition technology leaving many states
without legal restrictions on the government’s use of the facial recognition technology systems
(Purshouse & Campbell, 2022).
Solution
How privacy is currently being handled in the United States
Race, Again: Facial Recognition Technology 10
In the United States, the Constitution hosts principles that are good for American privacy.
The Fourth Amendment prevents the government from conducting searches that are not only
unreasonable but also without probable cause to obtain a warrant (Purshouse & Campbell, 2022).
Even though that is the case, law enforcement can use other means of collecting data. Law
enforcement agencies can purchase personal information from places like data brokers and
collect data from public places that do not have an expectation of privacy such as social media
sites (Purshouse & Campbell, 2022). Not only have citizens voiced their concerns on the matter
for certain cases but the Supreme Court has also acknowledged that technology in surveillance
needs to be examined further through the lens of the Fourth Amendment’s limitations (Purshouse
& Campbell, 2022).
In the case of Riley v. California in 2014, information that was obtained through a cell
phone was examined. In this case, an officer searched David Leon Riley and took his cell phone
as evidence in the case against him (Keenan, 2021). During the case, it was ruled that without
the presence of a warrant or pressing circumstances, the search for David Leon Riley’s cell
phone could not lead to his arrest (Keenan, 2021).
Another case that is similar to the case of Riley v. California, is the case of Carpenter v.
United States. In this case, the FBI used the cell phone numbers of the robbery suspects to gain
their cell site data and ultimately track Timothy Carpenter’s movements (2018). During the case,
it was found that the law enforcement officers did not obtain a warrant before they tracked
Timothy Carpenter’s phone. It was ruled that there needed to be a warrant to access the cell site’s
location information that is acquired when a cell phone connects to a cell tower (2018). The
judge at the time, Justice Anthony Kennedy, explained that just because law enforcement
agencies have the ability to access a cell phone’s location and its location records, it does not
Race, Again: Facial Recognition Technology 11
mean they should search freely (2018). He also states that there is no reasonable expectation an
individual has when it comes to their privacy and the records that are controlled by cell phone
companies. The decisions made by the courts in both of these cases point to the need for better,
stronger, and clearer laws when it comes to digital privacy. There needs to be safeguards
implemented to avoid the abuse of digital information to protect the rights of citizens and the
Supreme Court’s ability to implement these privacy principles is limited because they can only
use their judicial interpretations.
Along with the need for laws pertaining to the use of digital surveillance technology,
there needs to be a federal and more consistent national standpoint of technology and the
continuation of new advancements. Another issue faced due to technological advances is the use
of drones for law enforcement use. One case that addressed the use of aerial surveillance is the
case of Florida v. Riley in 1989. In this case, law enforcement was able to acquire a tip that a
suspect, Michael Riley, was growing marijuana in his Florida home backyard (2018). Based on
that information the information acquired by a source, Florida police department then proceeded
to surround the property of Riley, flying the drones at an altitude of four hundred feet (2018).
The police officers could see, through the drone’s surveillance technology, what they believed to
be the presence of the marijuana plant and ultimately charged and arrested Michael Riley. In this
case, it was ruled that. Since the drones were allowed to fly at that altitude, lower than five
hundred feet, the drone was flying in legal airspace and was allowed to fly over the property.
Based on the Florida v. Riley case, the ongoing flow of cases surrounding drone- use will
ultimately need laws.
A Florida case demonstrated the problem of using facial recognition technology on the
identify of suspects. The earliest court case on facial recognition technology was in 2015 called
Race, Again: Facial Recognition Technology 12
Lynch v. State in Florida (2021). In this case, an undercover officer bought cocaine from an
individual who goes by the nickname, midnight (2021). A black man by the name of Willie Allen
Lynch was identified by the police through a facial recognition system and charged for the crime
(2021). The harsh fact of this case was that there were four other erroneous matches that the
program returned, and law enforcement decided to choose Lynch (2021).
The Supreme Court has heard several cases on technology, new or old, regarding
geolocation technologies. Regardless of this, there are still many legal uncertainties pertaining to
today’s technological advancements like facial recognition technologies, where the judicial
history is very limited. All these cases prove that the judicial branch cannot manufacture privacy
expectations alone. There are a multitude of ideas that are currently being pursued to address
these issues and inequalities that specifically target the disadvantages of technological
advancements now and in the future.
Policy
The first change that can be made to these new technological advancements is the
requirement for these systems to be trained on diverse and representative datasets (Lohr, 2022).
The standard of training for the technological databases is predominantly white and male (Lohr,
2022). Using different skin colors and genders can eliminate racial bias in these systems and
further the advancement of these systems. Even in the technology of default cameras, settings are
not equipped or optimized to capture darker skin tones (Lohr, 2022). This can lead to the result
of lower-quality images in databases for African-American individuals. Having the standard
image quality raised for facial recognition with better settings for the photo capturing of African
American individuals can also assist in the solution to end racial bias in technological advances.
Race, Again: Facial Recognition Technology 13
Another change that can be made to these new technological advancements is the
notification, consent, and payment to individuals whose images will be a part of the testing on
technological system databases.
Lastly, holding companies accountable for remaining to use biased technology. An
assessment of performance, regular and ethical auditing, and not intersecting identities, should be
held just as much as building inspections (Lohr, 2022). Legislation can be used to monitor facial
recognition technology and other technological advances. As more and more advances begin to
be produced, for instance, the advancement of facial recognition algorithms, the new advances in
surveillance should be monitored.
Do nothing Approach
Doing nothing about facial recognition technology and the issues that arise around it can
and has become dangerous for the individuals this phenomenon affects. There is an active case
about a man who claims he was falsely charged with theft and improperly arrested because of the
police’s use of facial recognition technology. A man from Georgia by the name of Randal Quran
Reid was mistakenly arrested on November 25, 2022, for theft in a state he claims to have never
been to, Louisiana. Officers used facial recognition technology to identify the suspect (Reid) as
the individual wanted for the use of stolen credit cards to buy $15,000 worth of designer purses
in Louisiana. After spending nearly a week in jail, chargers were dropped but Reid is still suing.
If there is nothing done to change these systems, more and more individuals will be charged for
crimes they did not commit. This leaves this approach useless.
Incremental Approach
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The Incremental approach is an approach that breaks down each step in the decision-
making process and works on the task in steps. This is not ideal because individuals are being
held accountable for things they have not done which can have a big effect. However, this is the
best step because engineers can work on bettering facial recognition software and receive
feedback accordingly. In this approach, engineers are given the ability to test methods before
fully implementing them and have commitments that are minuscule. This process can take a long
time but is most beneficial in the long run.
Full Implementation Approach
The full implementation approach is ideal but not realistic. This is because a simple
software update can not fix every device overnight. Also, there are still some benefits to facial
recognition technology as we use them today. Riding all technology of facial recognition
technology, for example, unlocking a smartphone, is unrealistic because it is already being used
in everyday life. This makes this approach tough because there is no way to fully extinguish
facial recognition technology and replace it with an improved version instantly. It will take time,
practice, and feedback.
What is currently being done to solve these issues?
Currently, there are multiple groups that are advocating for these technological solutions,
demanding accountability and transparency from tech engineers, and educating lawmakers on the
current racial literacy in facial recognition algorithms. The organization, Safe Face Pledge,
mission is to get organizations to address the biases in their algorithms and further investigate
Race, Again: Facial Recognition Technology 15
their applications. In 2019 an act called the Algorithmic Accountability Act, empowered
authorization to the Federal Trade Commission to regulate and monitor companies to assess their
algorithmic training, and the accuracy of their technical performances, and better regulate their
data privacy (Kerry et al., 2022). There have also been several Congressional hearings that have
been held for the consideration of anti-Black discrimination in facial recognition technology and
there is a bill that was introduced by Congress members for the establishment of stipulations in
the restraints of the use of facial recognition technology (Kerry et al., 2022).
There were also many responses from multiple tech companies. A company by the name
of IBM discontinued all their systems due to their new knowledge of bias in the system (Kerry et
al., 2022). Another company is Amazon. Amazon has frozen their technology on facial
recognition use for police officers for one year, so their company can figure out a plan to fix the
racial bias in the technology system (Kerry et al., 2022). Microsoft is another company that is
making changes in their technology. Microsoft has stopped all sales to law enforcement agencies,
on on their facial recognition technologies until there are federal regulations installed for all
systems (Kerry et al., 2022).
With the support of these companies, there is a current advancement in the stride to more
progressive legislation. The movement to see a change in facial recognition technology and its
bias system is a long one. It is also connected to the equitability needed in the criminal justice
system.
Law Enforcement Assurance
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Impact on Department
A change in the implementation of facial recognition technology can have an effect on
police departments. Police departments will have to resort to only using facial recognition
software as a baseline for their cases and not as factual information until there is an update in the
systems that can incorporate different skin tones. As stated previously, facial recognition
technology is more advanced than the human eye but is not always 100% accurate at all times.
Facial recognition technology has trouble identifying individuals with darker skin tones,
individuals with similar facial features, and individuals wearing masks or impersonating another
person. Police officers using this technology as factual information can inflict upon the lives of
the innocent.
Impact on community - police Relations
In this day and age, tensions are high between departments and the communities. Due to
this increased tension, facial recognition can and has, added to the intestines negative thoughts of
law enforcement officers towards individuals of certain racial backgrounds. If police departments
do not use facial recognition technology as factual information, it can help demolish the many
officer stereotypes, racial profiling, and community fear and in turn help with the establishment
of a better relationship between community members and law enforcement officers.
Impact on Budget
Race, Again: Facial Recognition Technology 17
There are many positives to incorporating and regulating facial recognition technology in
law enforcement agencies that can help the tension between law enforcement officers and the
community. If that is going to be implemented, there needs to be a budget set in place. There are
two directions department leaders can take when it comes to the budget for implementing new
and updated software and technology in the agencies. They could understand the need for change
and develop a budget plan that can incorporate the new. Some department leaders could also
want money for other things and not believe there is a change that needs to be made which can
lead to destruction in the long run.
Conclusion
Facial recognition technology has become a significant implication in, not only the
criminal justice system but the lives of everyday citizens. Within these systems, there are biases
that are racially motivated due to inequality during the system’s development. Discussed in the
paper are the problems, solutions, and current movements in identifying and eliminating the
imperfections of racial bias in artificial intelligence and machine learning programs. The Bible
states in Mathew 7:1-2, “Do not judge, or you too will be judged. For in the same way you judge
others, you will be judged, and with the measure you use, it will be measured to you.”
Race, Again: Facial Recognition Technology 18
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