The Arguments for and Against Using Facial Recognition Technology in Public Spaces

The Arguments for And Against Using Facial Recognition Technology in Public Spaces

Arizona State University

CRJ 201 - Crime Control Policies and Practices

Date

The Arguments for and Against Using Facial Recognition Technology in Public Spaces

Introduction

Facial recognition technology is one of the hottest debatable digital technologies

currently being employed in the public sphere. With the growing use of CCTA by governments

and other actors in policing, transport and commercial service provision, scholars have drawn

some urgent questions about the ethical, legal and social ramifications. Brey (2004) claims that

facial recognition enables organizations to interfere with traditional notions of autonomy and

informed consent since individuals can be monitored without their knowledge when it is in a

setting like a street where they can barely make such a choice. A pervasive like this, usually

unseen type of surveillance, turns the public space into the location of omnipresent data

harvesting, which leads to discussions about the extent of technological agency and the

responsibilities of institutions executing these instruments.

Recent academia also notes major regulatory, human rights and security issues, which

arise when facial recognition is operated at scale. Solarova et al. (2023) highlight that the

majority of the countries do not have harmonized laws that regulate gathering, manipulation, and

storing biometric information, thus exposing people to abuse and inaccuracy. Also, Moraes,

Almeida, and Pereira (2021) caution that systems that are deployed within semi-public spaces,

including shopping centers and transportation hubs, will pose the threat of discriminatory

profiling and a failure mode of creep where data meant to be used in a certain way is silently

repurposed in a different manner. These issues highlight the immediate necessity to consider the

positive and the negative aspects of facial recognition, prior to the full acculturation of its

application in the realm of mass culture.

The use of facial recognition technology in the public space is a widespread phenomenon

in developed countries such as the USA, the UK, China, Australia, and African countries. In no

time, all countries of the world will be using this technology in all public spaces. According to

Fraser and Williams (2009), facial recognition technology involves software meant to identify

people by comparing the faces against a database of records. It is common among police

departments, shopping centers, railway stations, and airports across the world. It is also

commonly used in school districts, among other places. Fraser and Williams (2009) add that

there have been rising concerns that facial recognition technology inordinately misidentifies

people of color and it violates peoples’ privacy rights. It has led to its ban in some places such as

Oakland and San Francisco in the United States of America to police authorities and other

agencies of the government. However, even though these concerns increase, companies

manufacturing these technologies continue manufacturing numerous gadgets each day. Some

support the use of this technology in the public space and those who argue against it. This essay

discusses the arguments for and against the use of facial recognition technology in public space.

Arguments Supporting Facial Recognition Technology

The proponents of facial recognition technology cite numerous advantages of the

technology, which they believe supersedes its disadvantages. The reason why facial recognition

technology is supported to a greater extent by its proponents is the vital role it plays in security in

the public space. According to Fraser and Williams (2009), facial recognition technology has and

can prove to be successful in preventing identity theft and fraud. In 2020, for instance, it was

cited that the use of this technology by companies such as Facewatch facial recognition systems

allowed it to identify and stop 217 fraudulent transactions. It enables companies and

organizations dealing with data and other services that require user identities, such as those

issuing driving licenses, to halt the issuance of such services to fraudsters. In most of the

fraudulent cases reported in the United Kingdom, half of them involve identity theft which

cannot be recognized without the help of facial recognition technology. It then means that this

technology has served a fundamental role in curbing identity theft and fraudulent transactions,

which causes losses and insecurity.

According to Fontes and Perrone (2021), facial recognition helps gain investigative edge

in fast-paced scenarios police work. Automated recognition systems are said to expedite requisite

identifications needed by officers during critical real-time threats. Officers are updated in real

time about possible suspects who can be verified faster than traditional methods. Algorithmic

performance is said to exhibit steadiness that is particularly beneficial in large unpredictable

crowds (Fontes and Perrone, 2021). The organizations centered around emergency response

often require systems that can lessen the waiting time associated with manual data base queries.

As stated by McClellan (2019), real time data systems are vital when an officer has to make

quick decisions involving multiple leads. Staff working in congested settings appreciate systems

that increase monitoring with little or no increase to the personnel. The ability to divert officers

from monotonous tasks to systems automation is a commonly cited benefit. Improved systems

lead to better coordination among teams operating under the same tight timeframe. The

operational adaptability offered by the technology is seen by many as a necessity for law

enforcement agencies operating in complex environment.

Gikay (2023) claims that the standardized biometric practices minimize inconsistencies

that are evident when the officers are left to their own devices. Standardization helps the

agencies to create procedural consistency that promotes justice in the multi-stage investigations.

The majority of the supporters claim that standardized systems do away with the disintegration

of the identification practices that complicate interjurisdictional collaboration. Shared protocols

allow investigators to reduce the number of errors that are caused by the disparity in local

training standards. Andrejevic and Selwyn (2020) state that the agencies dealing with cross-

regional cases often use harmonized systems that explain communication and data formatting.

The advocates appreciate regulatory frameworks that promote gradual changes that perfect

operational applications without straining institutional capacity. The introduction of gradual

implementation periods allows jurisdictions to experiment with the performance of the system

under controlled conditions that build credibility early. Accountability is strengthened by

oversight structures, which also guarantee the predictability of the technology in diverse

deployment settings. Advocates argue that planned deployments lower the level of uncertainty in

the community and demonstrate the practical dependability that biometric systems can offer.

Prevention measures are usually supported when technologies identify alarming actions

at an early stage. The early detection can diminish the intensification of the harmful incidents

that develop rapidly in the crowded settings. According to Neroni Rezende (2022), Authorities

operating in crowded social places usually enjoy automated surveillance that is meant to ensure

that situations are under control. Most of the proponents feel that continuous operating systems

improve protective measures that cannot be maintained by human observers. Distress or agitation

indicators through behavioral means can be used to make timely interventions that reduce the

total risk. The advocates often claim that preventative frameworks correspond to more general

security models that focus on harm reduction (Barrett 2020). Public areas with continuous traffic

demand equipment that assists the authorities to distinguish normal behavior and possible threat.

Biometric surveillance is a source of vital information that augments decision-making among

officers who manage the rapid response. Societies with high attendance rates or seasonal

activities usually demand high-level surveillance abilities that increase safety planning.

According to Andrejevic and Selwyn (2020), the advocates consider educational

environments as the places where biometric authentication is advantageous. Access systems are

automated to restrict unauthorized access and enhance uniformity of day-to-day supervision

within campus areas. When attendance tracking is incorporated into secure digital processes,

faculty members tend to like less administrative work. Most of the proponents point out that

trustworthy identification helps in emergency response planning by ensuring that students are

present in the right way (Andrejevic and Selwyn 2020). The school staff that operate in high

populations need reliable tools that will be vigilant even when the staff availability is changed.

Advocates feel that early exposure to sophisticated systems enhances the knowledge of the

students on technological infrastructures that define the contemporary life. Children who are

growing up in more digital societies become familiar with safe systems that form the basis of

identity verification standards. The proponents usually focus on fair safety measures that are

formulated based on open execution with the assistance of community dialogue. It has been

argued that properly controlled systems can strengthen security in schools without compromising

the educational agenda and student freedom (Andrejevic and Selwyn 2020).

Facial recognition allows to increase accountability through recording interactions with

greater clarity and accuracy. The more identities are verified in a system during an incident, the

more the civilian complaints and officer reports have an evidentiary support (Gentzel 2021).

Most of the supporters emphasize that proper verification safeguards both the wrongly accused

and officers against false allegations. Biometric systems enhance the accuracy of identity

significantly because administrative agencies that provide public benefits often depend on

identity accuracy. Reduction of frauds is achievable where verification processes identify

anomalies that are often missed in manual checks. Perritt Jr (2020) argues that the government

institutions dealing with sensitive programs enhance integrity by ensuring that the loopholes in

documentation are not exploited repeatedly. Advocates emphasize that automated records

decrease the human factor, which influences the accuracy of data in bureaucratic processes.

When agencies are shown to be responsible in their use of tools that are aimed at improving

accountability, then the confidence of the people may be boosted. It is a positive outcome when

communities see institutions embrace transparent practice that reinforces uniformity in identity-

dependent services.

Shore (2022) notes that the acceptance level of the general population is higher when the

face recognition is positioned in the context of convenience and not security. Most citizens enjoy

the fact that they do not have to wait long when they are seeking transportation hubs that demand

identity checks to enter. Frictionless authentication provides quicker transit via the communal

amenities that provide the necessary services to the hectic populations. The government offices

that adopt streamlined check-in systems might be more efficient and help both the staff and the

visitors (Stajic, 2015). Many proponents emphasize the fact that streamlined procedures facilitate

autonomy by eliminating the workloads related to document management. People who have to

travel in the busy urban areas often embrace technologies that minimize delays that make

everyday lives very difficult. Experience with biometric systems can help to reduce the fear of

more advanced deployments built later. Technologies are frequently seen to increase public

comfort by demonstrating practical benefits and then move to larger security applications. The

advocates feel that convenience-based messaging creates early trust that helps in long-term

adoption plans within municipalities.

The citizens are more supportive of facial recognition when cooperation is established at

an early stage. The community engagement promotes the clarity of the residents in the

deployment goals and posing the required questions. When community’s express expectations

based on local experiences, policymakers tend to get a good idea. Bragias, Hine, and Fleet

(2021) say the advocates believe that the collaborative dialogues reduce the fear of surveillance

by making clear the existing protection. The forums with the public enable the authorities to

respond to the misconceptions that tend to increase the lack of trust among the suspicious groups.

Advocates argue that involvement brings about collective responsibility which enhances safety

programs in the neighborhood in the long term. According to Andrejevic and Neil (2020), the

citizens who observe transparent planning feel empowered in the process of formulating

guidelines, which affect deployment contexts. The implementation of the community viewpoint

is usually easier with agencies that incorporate the community view, thus reducing backlash and

misinformation. Collaborative governance decreases uncertainty through showing respect to

public concerns that influence technology-related decisions.

The flexibility reinforces the claims in favor of facial recognition in the growing smart-

city systems. Integrated networks that run in transportation networks are based on coordinated

identification that improves coordinated responses at the municipal level. Emergency response

agencies in cities tend to need fast information exchanges that biometrics provide reliably and in

a short period of time (McClellan 366). Real-time data is also stabilized by integrated

authentication that is useful in environmental monitoring platforms. Advocates point to the fact

that smart-city ecosystems require robust identity tools that ensure that disruption in one service

does not affect other services connected to it. According to Norval and Prasopoulou (2017) says

municipal departments working together using unified systems have better alignment that

enhances overall operational effectiveness. Biometric compatibility is being explained by many

proponents as a necessity to cities that face the increased population density challenge. The

interoperable frameworks assist the urban centers to cope with the strain on transportation,

utilities, and emergency infrastructures (Kukielski 2022). Facial recognition is often considered

an important structural element that facilitates coordination by policymakers who consider

modernization initiatives.

Public Security and Crime Prevention

The second argument that has been presented to support the facial recognition technology

is that this technology provides people with security. The general area is accessible to all people

because there are no limitations on the individuals that access it and those that should not access

it (Khan et al., 2018). It renders such places to be available to occurrence of crimes that are

damaging to the security of other users. Phillips et al. (2018) suggest that it is important to ensure

societal order that can be realized by even enhancing facial recognition technologies that involve

cameras and other technology tools to enhance the efficiency of security in social settings. The

police in London have been able to recognize wanted and wanted suspected criminals in the city

streets through the use of facial recognition technologies like the camera, there are several

occasions where such technologies have been applied to the streets of London. Thus, this

technology has been critical in capturing the image of individuals enabling them to compare the

images that are present in the databases of the police in the effort to identify the criminals in real

time. Bradford et al. (2020) further explain that crime has been cut down by 60 percent due to the

adoption of the fail recognition technology in the open space in the United States, which is part

of the effort to achieve a secure communal area to all. It is noteworthy that this means that the

technology is increasingly significant in the enhanced security of the public areas throughout the

world.

The issues of facial recognition in relation to the coming of spatial design, behavioral

expectation and guardianship density are taking the center stage in urban debate on matters

related to public security. The Crime Prevention Through Environmental Design (CPTED)

research presents how sightlines, access points, and pedestrian circulation in the shared space are

structured in a way that affects the possibility of risk development (Cozens, 2013). City leaders

who consider biometric devices often consider them as giving supplement to these spatial

processes by adding new observational strata which surpass the boundaries of human

surveillance. The outcome is some hybrid type of guardianship where environmental signals are

inter-connected with algorithmically created notifications which were able to attract attention to

micro-spaces with rapidly changing risks. In the same way, private security functions are moving

in that direction and their functions are becoming more data-centered and combine spatial design

knowledge and monitored metadata technologies (Stajic, 2015). Integrated within such more

general design policies, facial recognition is a process that can assist city administrators to

predict changing attitudes of presence, allowing them to restructure urban spaces in advance

before the gaps harden into incivility opportunities.

The process of improving community safety is often discussed publicly with the view to

the necessity to have the systems that could convert the daily manifestations of uneasiness into

the specific, responsive actions. According to crime prevention theorists, lasting safety initiatives

require authentic engagement between institutional participants and residents particularly in the

context of perceived disorder that is subject to constant interpretation (Crawford and Evans,

2017). Numerous locals find it difficult to evaluate uncertain behavioral trends, and access to

digital tools of identification may ease any reluctance by explaining whether the recurrence of

appearances or movements of a certain type exhibits familiar trends or not. Routine activities

will stabilize once the amount of uncertainty is reduced, and shared spaces will become more

predictably exploited. Environmental criminology literature observes that the perceptual

ambiguity is reduced which leads to a decrease in fear of crime and development of a sense of

collectivity dominating the spaces of the public (Lee et al., 2016). Facial recognition can

contribute to reinforcing these community feedback loops by creating clear information about

presence and movement that the authorities can then use to focus patrols or how problematic

areas can be redesigned or preventive resources allotted with a greater level of precision.

Overcrowded areas consistently create issues of enforcement difficulties due to small and

unmonitored periods that would create perfect conditions to enable opportunistic criminality.

According to routine activity theory, criminals are strategic and capitalize on the periods when a

viable guardianship fails temporarily and particularly in areas where traffic is high and people

move in all directions (Felson, 2017). This tactical assessment is interrupted by a facial

recognition system that heightens the prospects of perceived continuity of observation by

diminishing the chances of offenders feeling that they can commit an act without detection.

When this adjustment takes place, the behavioral disequilibrium in the public settings is changed,

and prospective offenders take into consideration the increased accountability when making their

decisions. The situational crime prevention research highlights that by changing the cognitive

scripts offenders operate under, including but not limited to latent anonymity or the ability to get

away, one can achieve quantifiable crime opportunity reduction in complicated environments

(Clarke and Bowers, 2017). Facial recognition, in that way, is put within the framework of a

larger prevention program, which realigns the way offenders perceive crowded places, making it

more difficult to commit spontaneous or impulsive offending in this case.

Along with a greater reliance on analytic systems that can rebuild detailed movement

paths leading to an incident and the, movement paths leaving it, there is the increased reliance on

investigative practices built on the basis of analytic systems. The research on the efficacy of

CCTV reveals that filmed video footage is enhanced in strength when combined with systematic

analytic instruments by which the review procedures are managed, allowing investigators to

efficiently follow the patterns of conduct in the case (Piza et al., 2019). The faculty of facial

recognition enhances this ability by providing an accurate means of connecting people in a

variety of places, and thereby minimized the confusion that can continue to defy investigations

into the past. These improved data flows over time can support but go beyond supporting

investigations, and significantly, are a part of long -term performance appraisals on the use of

public space. Third-generation CPTED theories state that evidence-based redesign is adaptive to

ensuring safe urban spaces, especially when the patterns of use change or new disorder forms

arise (Mihinjac & Saville, 2019). Facial recognition therefore aids in a cyclic process of

evaluation as authorities can discover spatial arrangements that persist in creating risk and will

invite strategic changes that will strengthen the larger objectives in crime-prevention.

Urban scholars are becoming more interested in the use of biometric technologies to

guide the process of shaping of spaces inhabited by people in urban areas as the patterns of

moving change over a day. According to CPTED concepts, the change in the environmental

conditions that have an effect on crime is subject to the fluctuation of pedestrian numbers in the

surroundings, the lighting as well as types of activity (Crowe and Zahm, 2015). A facial

recognition system installed in such settings can provide an unprecedented understanding of the

movement of people in a particular environment, enabling the planners to identify minor changes

that can signify the emergence of weaknesses. Such changes are usually used in the

determination of guardianship reinforcements or architectural modifications that may be justified

in order to avoid the hiding of opportunities or airstrips. Studies that examine the crime

prevention models highlight the fact that adaptive responses could best be effective when they

combine spatial planning with a continuous study of human activity (Mackey & Levan, 2013).

The capability of facial recognition generating real-time identification data transforms to an

instrument to judge the authenticity of specific design interventions as effective to alleviate

criminogenic states. The technology aids in the creation of a more dynamic type of prevention as

the planners continue to perfect these environments whereby the built structures and monitoring

systems will buttress each other to stabilize the public order.

Layered strategies, which may be deployed by the public security at all times, may be

used to combat crimes that may be unobtrusive instead of courteous confrontation. There are

some types of misconduct in public areas such as harassment, targeted following,

reconnaissance, which often happens in a manner that is invisible to even our traditional patrols

yet remains to offer serious safety risks. Crime opportunity theories underline that criminals are

dependent on the predictability of relationships with the environment to determine targets or if a

place makes it possible to do crime without being noticed (Felson, 2017). The use of facial

recognition undermines these ratings very subtly since the database uncovers recurring patterns

of presence which would have been deemed random. Once these patterns are identified, the

police or community safety officers are able to ensure that the behavior is dealt with before it

becomes an explicit offending behavior. CPTED models also emphasize that the success of

prevention is often based upon finding early warning signs of a situational threat rather than

reaction to damage (Armitage, 2016). Whenever employed in this proactive nature, facial

recognition aids in detecting micro-behaviors that provide levels of preparation of crime, which

enhances the preventative elements of the public-space micromanagement.

With the aim of developing resilient systems of public-space safety, predictive analytics

are finding more and more applications to detect changes in the environmental hazard. The third-

generation CPTED scholarship suggests that to prevent crime effectively, socially informed,

data-infused, diagnostics, linking the physical design with the pattern of social interaction should

be applied (Mihinjac & Saville, 2019). Facial recognition is part of that process of diagnosis, as

it is mapping and identifying common patterns of motions and when we experience a congestion

or loss of vision or even discord in routine. These lessons would enable safety practitioners to

create interventions (e.g. lighting changes or reshaping entry points) with a better sense of social

routine that constructs vulnerability. Research in crime prevention also proposes that those

locations where patterns of activity are regular and well known leave fewer chances of covert

offending (Cozens, 2013). Facial recognition is used to clarify such patterns in ways that can

hardly be achieved by traditional observation. By making such decisions based on such a map,

the spatial distribution of guardianship, or even the timing that is used to minimize the risk of

offending in a community space, the outcome provides a more dynamic and situation-based

approach to crime reduction.

Police coping with sizeable, multifunctional public spaces in general tend to face the

challenges of under allocating available resources to the security needs, which are periodically

changing. Literature on the crime prevention emphasizes that the allocation of resources will be

made more efficient, when the allocation relies on fine-grained, empirically determined

indicators of risk, as opposed to the course of assumptions of the disorder (Mackey and Levan,

2013). Facial recognition also provides a way of generating such indicators by determining

which places constantly seem to attract the same persons during the odd times or when it comes

to events that cause a lot of stress. Once these forms of space-temporal clusters are detected, the

agencies will be able to modify patrol patterns or assign particular forces to those regions in

which the risk of dangerous incidents occurrence is high. Scholars who majored in community

note that accuracy in the allocation procedure also builds trust, as residents begin to feel that

interventions are based on perceived needs, and interference does not manifest itself (Crawford

& Evans, 2017). Facial recognition, in this way, becomes a part of a dedicated preventive

framework that coordinates staff, surveillance equipment, and the physical layout as per

particular, evidence-based weaknesses, leading to more effective and socially responsible

security measures.

Digital Identity and Scoring Systems

Digital identity and scoring systems are becoming the most fundamental ways in which

governments, agencies, organizations, and companies worldwide operate with ease. The

proponent of facial recognition technology argues that this technology is helpful in the

establishment of scoring systems and the creation of digital identity, which improves the delivery

of services. Williams (2004) indicates that China’s government is creating a social credit system

using this technology to analyze individuals’ behavior in the public space, helping them

determine behavioral trends by creating digital profiles. Vande Walle, Van den Herrewegen and

Zurawski (2012) assert that these profiles are then used to deliver services such as the provision

of loans and others services based on such behaviors as compared to existing standard moral and

ethical parameters. It, therefore, allows such governments and institutions to reward and punish

individuals based on their scores. This technology also will enable governments and

organizations to identify systems by integrating personal information, including work addresses,

residence, biometric data, and other details to create single identity cards (Williams, 2004).

These identity cards serve a crystal purpose in disseminating data and information specific to

individuals hence reducing the complexity in service delivery. It reduces lengthy procedures,

saving time. Based on this evidence, it is clear that this technology saves time and makes service

delivery effective and efficient.

Scoring and more and more digital identity is dependent on environmental surveillance

systems that are much more than mere authentication systems. By incorporating facial

recognition into such systems, the institutions can link behavioral patterns and spatial patterns

that have already been studied by authorities in the public with the aim of ensuring safety. The

study of environmental design shows that a location with a high degree of surveillance can also

transform the everyday interactions of people and affect how people move and use the city (Lee

et al., 2016). This interaction is not foreign to the logic of digital scoring models which not only

judge people based on individual actions but on movement patterns based on environmental data.

Crowe and Zahm (2015) also state that the environmental monitoring frameworks provide

ordered conditions in which the institutional control would be more orderly. Such systems

incorporating facial analytics increase the potential of the state to project identity into space.

These mechanisms increase more doubts regarding the behavioral categorization because the

spaces that were to be used as a way of preventing crime become a profiling instrument. This is

an example of how digital identity systems implicitly combine the goals of public safety with

larger evaluative roles.

Digital identity systems use more and more of the situational crime prevention strategies,

especially those based on the power of control of the environment and predictability of behavior.

According to Clarke and Bowers (2017), situational frameworks are designed to control

opportunity structures, and not intentionality, but the facial recognition scoring system reverses

the logic, requiring behaviors to be fixed identities. This inversion creates a new critical ground

whereby people are constantly evaluated by algorithmic standards instead of situational

circumstances. Focusing on the values of social cohesion and environmental well-being,

Mihinjac and Saville (2019) point out that further CPTED practices may be directed at

converting them into numerical scores not associated with human beings, whereas a digital

scoring tool poses a threat of doing so. Felson (2017) continues by noting that the pattern of

routine activities is dynamic and socialized, meaning that computational scoring mistakenly

believes that natural social variation is deviance. This is because structural disadvantages can be

even placed on people because these digital identities are more dependent on such evaluations

and founded on inaccurate behavioral cues. It is a change that shows the incompatibility of

environmental crime prevention models with the broad ambition of digital identity technologies.

The increasing use of facial recognition to check identity also restructures the distribution

of trust within institutions. Digital scoring systems grant credibility not based on interpersonal

reputation but according to algorithmic assessments summed up on the results of surveillance.

Armitage (2016) states that the environmental design-based crime prevention strategies

traditionally focused on inquiry of natural surveillance instead of the strict categorization. But

once the facial recognition is incorporated into such infrastructures, it will be possible to trace

the shift between observation and judgment. Piza et al. (2019) show that surveillance

technologies do not leave a quantifiable impact on crime reduction, but such systems do produce

large amounts of data that can be reused to score identities. Repurposing transforms the role of

surveillance into administrative evaluation rather than safety, and changes the interactions

between the masses and institutions in hidden manners. The people are becoming more

dependent on the use of algorithm systems to prove their social legitimacy, which reduces the old

trust building forms. These processes give rise to new kinds of hierarchies of reputational value

located in the context of the daily interactions.

The second implication of digital identity scoring is the conversion of the public spaces

into the arenas of evaluation. Facial recognition is continuous, and it allows institutions to rank

people on the basis of fine behavioral indicators derived out of their spatial behaviors. Lee et al.

(2016) demonstrate that environmental conditions affect the way individuals act in the observed

places, however, the situations resulting in such practices are scoreable as consistent evolution of

character. Crowe and Zahm (2015) note that the purpose of environmental design is to minimize

fear and maximize safety, whereas the implementation of facial scoring places pressure on acting

in an acceptable way to prevent punishment. This changes the psychological nature of the public

space and transforms it into a reputational risk site. Mihinjac and Saville (2019) insist that third-

generation CPTED focuses on livability, but identity scoring invalidates this by implementing

intangible evaluation systems which restructure social comfort. The overall consequence is some

force of discipline which will shape occupations of space, making people conform and suppress

spontaneity. These consequences imply that there are radical transformations in the social

functioning of the public environment.

Facial recognition-enhanced systems of digital identity also provoke issues regarding the

growing irreversibility of personal data. Digital scoring systems create cumulative identity

profiles, unlike the conventional surveillance records, which expand with each monitored

movement. According to Felson (2017), routine activities inherently change over time, but the

digital identity systems perceive them as quantifiable anomalies of normative patterns. Clarke

and Bowers (2017) also warn that situational indicators are usually misjudged by observers that

can cause misclassification in evaluative systems. These misunderstandings are entrenched into

long-term personal records, when transferred to identity scoring, and provide a permanent

disadvantage. According to Armitage (2016), crime prevention paradigms are historically

oriented at the adjustment of environment instead of characterizing the individuals, but the

concept of facial recognition inverts these priorities because the environmental data is turned into

the identity qualities. Records like this are permanent and this undermines the prospects of

rehabilitation, privacy and social mobility. This demonstrates an institutional imbalance in terms

of institutional memory and personal change ability.

The other issue that is emerging is the disproportion between institutional power and

citizen enlightenment in digital scoring systems. According to Piza et al. (2019), the surveillance

is usually not conducted in an open manner, and discrepancies in the flow of information

emerge, leaving people unaware of the way the data is being utilized. They may be rated by

systems that they cannot see, debate, or modify when they feed the opaque systems with digital

identity scores. According to Lee et al. (2016), environmental design is enhanced in terms of

validity by the inclusion of the population, whereas the identity scoring system eliminates any

meaningful involvement by automatizing the judgment. The role of the community is of the

highest significance to the construction of safe environments. The systems of scoring are based

on the efficiency of the institution, and this method ignores the fact that communities actually

have any say in the process. Since Crowe and Zahm (2015) state that asymmetry results in the

circumstances when behavioral debts are accumulated without being detected and eventually

affect the manner in which people can receive the required services. A lack of transparency

improves the structural vulnerability standards that discriminate the disadvantaged groups.

Public Health Protection Measures

The proponents of facial recognition also cite the impotence of this technology in the

implementation of public health protection measures. Public health protection is an essential

aspect because it ensures that the public is protected against the spread of infectious diseases. In

the wake of the COVID-19 pandemic, it is clear that individuals need to adhere to particular

public space behaviors and rules to ensure that the infection does not spread fast. According to

Faraj, Renno, and Bhardwaj (2021), facial recognition could be used to enhance public health

protection measures by monitoring the compliance with the need to use facial masks in public

space, the need to keep social distance, and also compliance with quarantine through the

installation of cameras in such areas. Chen, Marvin, and While (2020) adds that in some places

such as China, facial recognition technology can measure the temperature of individuals, making

it useful in monitoring and containing the spread of the virus. During a pandemic like the

COVID-19 pandemic, this technology helps identify those who disregard the rules, such as the

mask. As a result, it improves public health protection measures.

Facial recognition can also enhance the protection of the public health by enhancing the

accuracy and synchronization of real-time surveillance networks to react to rapid outbreaks.

Timely information is essential to preventing escalation by public health agencies, and the facial

recognition is connected with the existing information streams that foster the situational

awareness in overcrowded areas where human observation cannot be effectively applied. Morse

(2012) emphasizes the importance of a timely identification of infectious diseases to cut the

transmission chains, whereas Choi (2012) states that contemporary surveillance requires the use

of complex systems that can process various signals in parallel. This model is enhanced by facial

recognition which reduces gaps in observation which prevents early responses. According to

Gilbert and Cliffe (2016), the surveillance process relies on the tools that are capable of working

without interruption even in those situations when human resources are exhausted under the

emergency conditions. Constant working would help to identify the presence of abnormal

movement patterns close to health centers or supply points in the real-time providing a non-

contact way of preempting any hot spots. The insights obtained as part of this process contribute

to more preparedness measures and assist in more rapid adaptation to intervention measures.

Value is created where facial recognition assists in specific distribution of protective

resources in the times of high risk. The uneven distribution of interventions is also a common

issue in the field of public health, particularly when quick reporting of any susceptible group is

hampered by the fact that manual reporting is slow. According to Nsubuga et al. (2011), targeted

intervention requires the identification of the populations that will most benefit whereas McNabb

(2010) underlines that surveillance should be effective and dynamic to changing epidemiological

environments. Facial recognition systems can identify areas when noncompliance or high

exposure rates occur so that outreach crews and supplies can be more quickly deployed.

According to Lee and Thacker (2011), contemporary health intelligence advocates the

advantages of data integration elucidating the level of behavior in the population implying that

the technologies will be useful in decision-making pertaining to mobile clinics or vaccination

points. The characteristic of the system to sense local activity assists in more equitable resource

plans particularly in times of limited capacity.

Incorporating bio-metrics in emergency health communication systems can enhance

event compliance, as messages are ensured to be sent to the relevant audiences in real-time.

Facial recognition technology can be utilized in outbreak control through quick relay of

information. In line with this, Choi (2012) claims that the future of surveillance will depend on

the ability to integrate control of multiple systems. The rate of communication generated drives

the rate of change in the behavior of populations. Gilbert and Cliffe (2016) acknowledge that the

emerging healthcare system will allow a more dynamic approach to incorporate adaptive health

response systems. Enhanced communication fosters a substantial and close control of population

behavior.

Moreover, facial recognition is a technology that can be utilized in recondition periods

after an event. Residual risks may still be at issue, Nsubuga et al. (2011) argue, although large

outbreaks are decreasing. Long-term surveillance, in their view, can even recognize secondary

infections as well as the hotspots that will originate after the containment phase. McNabb (2010)

points out that intelligence in the global surveillance should rely on the continuous data

collection and not captures in short intervals. The ability of recovery to be tracked with the help

of systems that are linked to facial-recognition thus allows agencies to determine whether

communities are returning to safe behaviors or require further intervention. The insights such as

this empower the authorities to avert the untimely easing of safety measures that would thus

preserve the stability of the defense measures, which is what keeps a resurgence at bay. Lee and

Thacker (2011) point out that the merging of various data sources deepens the understanding of

health trends and therefore has the benefits of assessing the present level of community

resilience.

One of the good things that public facial recognition can do for public health is to make it

easier to assess the impact of environmental pollution and risky behavior in certain areas. In their

work, Coronado et al. (2011) stress the importance of environmental data in the accurate

identification and understanding of health outcomes. Through facial recognition, authorities can

find out how many people are in areas that are poorly ventilated, not well sanitized, and have a

lot of high-touch surfaces. Armed with such data, they are able to evaluate the risk levels linked

with different physical layouts by comparing them. Thurman et al. (2011) maintain that the use

of standard methodological approaches is one of the factors that results in the reliability of such

assessments. Once micro-areas with a situation of continuous congestion or deficient distancing

are detected, space utilization or traffic flow can be changed by planners in such a way that the

risk of exposure goes down. Gladden et al. (2014) point out that the so-called surveillance

systems, which are able to record the interactions of the population on different levels, are very

important. The kind of spatial analysis discussed here can be a great help to ambitious

environmental health programs, which are geared towards slowing down the spread of infections

in different kinds of public spaces.

One of the ways facial recognition can be helpful is by making the evaluation of health

interventions more efficient through its ability to provide uninterrupted feedback regarding the

safety measures' effectiveness. According to Morse (2012), the constant, cyclical evaluation is a

main factor that keeps systems for identifying diseases in a lively state. In addition, these data

empower public health officials to judge if the measures are effectively leading to the change of

behavior that was the goal (Choi, 2012). Facial recognition enables the analysts to record the pre-

and post-intervention adherence without using self-reported compliance which is usually prone

to bias. This fact assists in refining the campaigns, redistribution of staff or changing the

enforcement tactics. The technology can provide objective behavioral indicators and therefore

the programs related to the protection of the population health will be evolved based on the real

outcomes and therefore the efforts aimed at population protection will be strengthened by more

accurate and accountable measures.

Arguments Against Facial Recognition Technology

Some oppose the use of fail recognition technology in the public space. These opponents

cite various loopholes and weaknesses in implementing its use and the potential breaches they

cause. For instance, it states that facial recognition technology during the pandemics, endemics,

and public health crises is problematic. Julian et al. (2011) say it is because the collected data

from such technologies have to be crossed-checked with the person’s health information records.

It leads to the aggregation of a significant amount of sensitive information and data in a single

data. It raises concerns regarding the lack of consent from these individuals to use data is

collected regarding how their data is collected and stored. It also leads to problems on algorithm

bias and potential risk in the case of data breaches (Faraj, Renno & Bhardwaj, 2021). In other

words, it does not follow the rules on data collection, storage, and retrieval of sensitive personal

information. Therefore, even though it serves a crucial purpose in ensuring that public health

protection is enhanced, it causes serious concerns that supersede this function.

Privacy and Human Rights Concerns

The opponents of facial recognition technology also indicate that it violates human rights

and freedoms. It leads to the path toward blanket surveillance automation. However, the

widespread use of CCTV cameras worldwide, their use by the government and authorities in

public spaces raises concerns (Innes & Clarke, 2009). This technology brings a new level of

monitoring is used by the government and authorities. Innes and Clarke (2009) state that when

the authorities use it in the public space, it automates and indiscriminate peoples’ live

surveillance as they undertake their daily activities. As a result, it allows them to track each

citizen’s behaviors and moves, which breaches their rights to privacy and other freedoms

guaranteed by human rights (Innes & Clarke, 2009). Such violations contravene the human rights

provisions guaranteed by the constitutions in diverse parts of the world. Therefore, using these

technologies should be restricted to only purposes other than its use by the government to

monitor its citizens’ moves and behaviors.

Facial recognition technologies used in the public areas not only violate the right to

privacy, but also expect the appropriateness of the surveillance activity. Watt (2017) states that

mass surveillance however under the pretext of security is dangerous because it erodes the

expectations of anonymity in the society because of the tendency to normalize constant

monitoring. The use of surveillance systems in the public places an ever escalating boundary

between surveillance to ensure safety and monitoring activities of the regular citizens in an

intrusive manner. One of the main points that Nissenbaum (2020) makes involves the fact that

these technologies contribute to the issue of contextual integrity, where the personal data of

people is collected and utilized in a manner that was not originally provided as the context,

leading to ethical and legal tensions. This ongoing flow of data, which is not always informed

consent, increases the possibility of its misuse, both in the field of profiling to discriminatory

enforcement. As a result, the application of the facial recognition to urban spaces should be

evaluated closely in terms of human rights regulations and cannot be deemed as the cost of

technological progress at the expense of basic rights outlined in international and national

legislation (Bignami and Resta, 2018).

Outside the realm of personal and family issues, facial recognition technologies have the

potential to cause problems in different countries e.g. extraterritorial human rights obligations.

Bignami and Resta (2018) explain that states spying on people on a large scale should consider

the privacy of people even if they are not of their nationality, thus their responsibility goes

beyond their territorial borders. If multinational corporations offer facial recognition systems to

states, then the question of who is responsible becomes unclear, hence the problem of

jurisdiction and enforcement. In his article, Sullivan (2018) underlines that private and public

actors may change the way they collaborate so as to be less controlled by human rights and this

in turn creates legal loopholes where privacy violations can occur without being checked. Van

Zoonen (2016) also argues that the extended use of smart city technologies heightens these issues

because the interconnected systems not only share but also store the sensitive data that can be

easily accessed by different countries and corporations. The complicated relationship between

the public authority and the private technology providers emphasizes the necessity of strong

regulatory frameworks that would be able to protect privacy worldwide and at the same time

ensure transparency and accountability.

Additionally, the application of facial recognition intensifies the concerns about the

possibility of discrimination and social exclusion as a result of the technology. According to

Watt (2017), the most affected group of people in such a scenario may, in fact, be those with low

social status as, they might be exposed to a more rigorous examination. This discrimination

within the institutions undermines the equality before the law, the universality of the protection

of human rights. Drachsler and Greller (2016) note that analytics based on facial recognition are

susceptible to considerable error margins. When integrated with automated processing, this data

can lead to unjust outcomes, including false identifications or the potential for selectively

enforced legal measures. According to Carr (2016), the idea of public-private cooperation allows

rushing the incorporation of such technologies without approaching them with all these ethical

concerns in mind and prioritizing the efficiency of operations over human rights. The matter of

facial recognition is not only very personal by definition, but it can also contribute to the

intensification of the structural inequalities. This is why protective measures should be

introduced to prevent discriminatory outcomes and still keep the transparency of data

management and accountability in the algorithm processes.

Consent to public monitoring is a delicate problem, and the vast majority of individuals

are not able to make their choices regarding being omitted of the facial recognition. Nissenbaum

(2020) states that privacy in a public place is relative and social norms that a person is given

some form of individual freedom has been breached by mass surveillance. According to Van

Zoonen (2016), there are instances when by the application of smart city technologies as a way

of supporting urban management, the technology operated without the consent of the population,

resulting in a conflict of the ethical duty and the innovation. It deprives individuals of the ability

to control their personal information hence denying the right to human rights which are supposed

to be the foundation of human rights. Drachsler and Greller (2016) claim that despite technical

data protection systems of high quality, the absence of informed consent may not be completely

compensated due to the fact that data capture is, strongly speaking, an aspect of intrusion. It is a

message to legislators that besides the questions of technical effectiveness of the surveillance

systems, the question of the ethical side of the privacy, autonomy and trust to society needs to be

discussed and involved in the implementation of technologies, hence ensuring that human rights

remain central to technologies.

One of the major criticisms of the widespread use of facial recognition is that it is

blurring the line between the public and private spheres, which is a crucial feature of human

rights discourse. Sullivan (2018) observes that the regulatory structures that govern privacy are

often based on the idea of a clear distinction between the state and the individual's private life;

however, the omnipresence of surveillance weakens this border, as it forces the exposure of the

most intimate behaviors to the eye of the watcher. Watt (2017) states that the concept of being

actively monitored 24/7 is gradually creeping into the consciousness of society, so the entire

notion of anticipating registered observation turns into a rarity rather than a status quo. The

obligation to protect the privacy of users is shared with a number of different entities. As urban

areas become more sensor and data-driven, the pervasiveness of surveillance is expanding in

places that used to be considered neutral, such as streets and parks. According to Carr (2016), it

is challenging to identify the responsible party in relation to such systems as collaboration

between government agencies, representative of technology-driven companies. This shrinkage of

private space calls for the re-establishment of legal and moral frameworks to protect fundamental

rights from the invasion of the surveillance apparatuses that are taking over.

One of the possible sociocultural consequences of facial recognition technologies in the

future, as suggested in the literature, goes beyond the immediate privacy infringement and

touches upon democratic representation and civil rights issues. The enforced surveillance regime,

according to Watt (2017), can lead to the phenomenon of self-censorship, as people tend to

restrict the freedom of expression and assembly which they have in fact under the control of the

monitoring authority. The loss of trust in public institutions with a consequent chilling effect on

the democratic engagement is what Nissenbaum (2020) also points to, arguing that

depersonalizing surveillance will destroy the social fabric of the public. In their paper, Bignami

and Resta (2018) emphasize that the establishment of extraterritorial privacy rights for citizens as

promised by states is a sign that the lack of control over surveillance has consequences for the

entire planet. Drachsler and Greller (2016) state that even small decisions related to analytics

based on facial recognition may influence the policy-making processes and thereby boost

systemic biases and discrimination. These changes lead to the idea that the questions of privacy

and human rights that arise in connection with facial recognition are not only a matter of single

invasion. Their effects on democratic governance are so profound, in fact, that a precise tradeoff

between security objectives and respect for fundamental rights is called for (Gladden et al. 2014;

Coronado et al. 2011).

Lack of Regulation and Legal Risks

Innovations and their applications should be controlled so as not to be misused and to

make them more efficient tools. Opponents of facial recognition technology, however, argue that

in most countries globally, facial recognition technology is not regulated, and there is no legal

framework established for it (Andrejevic & Selwyn, 2020). Consequently, this point or loophole

has facilitated its abuse. Bradford et al. (2020) state that it is now more than ever that these

situations can be found in which individuals, organizations, and governments obtain people's

pictures without their consent or knowledge. In such a case, the pictures can be used for illegal

activities such as by hacking and unauthorized monitoring of the owners. Bradford et al. (2020)

add that the most common method of illegal photograph use is by unauthorized hackers. The lack

of regulation has increase concerns on the violation of human rights to privacy in public space

because the right to privacy protects individuals expressing themselves in public space against

unnecessary intrusion from private companies and states. It means that the use of this technology

in public space and the lack of proper regulation allow for large-scale recording and data storage.

It also enables the analysis of such data to identify individuals expressing themselves in a public

space against the provisions of the right to privacy (Chen, Marvin & While, 2020). It is a severe

concern evident through the way governments across the world use such technologies to identify

and punish protesters against the provisions of human rights.

The high rate at which facial recognition technology (FRT) is changing has led to the

current regulatory lag, where the rate of innovation has dramatically exceeded the creation of

appropriate legal frameworks to address it. The lag does not apply only to FRT; the introduction

of the drone technology did not exclude certain lags as the deployment of the technology was

utilized as a surveillance tool and as a means to map the environment without explicit legal

guidelines, which resulted in a conflict of airspace and privacy. Vacca and Onishi (2017) assert

that online, blockchain technology was developed with similar challenges, with the lack of a

concrete regulatory position rendering the solution uncertain and preventing its use as a secure

and transparent e-government (Batubara et al., 2018). This pattern is a failure of the systemic

character of governance to actively confront the legal aspect of disruptive technologies and

create the gap that corporate policy, frequently, takes the place of democratic lawmaking.

The situation of risk, not discussed critically, which can be quite deadly in this

unregulated field, one of such is that of the so-called regulatory arbitrage: technology providers

and users can take advantage of the differences between legal regimes in different jurisdictions.

The potentially constrained company can simply outsource its data processing or training of

algorithms to other territories with a less restrictive or an absent legislation. The practice

disaggregates data governance around the world and negates the work of regions that have

adopted strong protections, including the European Union data protection regulation named the

GDPR (Tikkinen-Piri et al., 2018). The same applies to telemedicine, and the cross-border care

delivery had to overcome a crowd of international licensure and data privacy laws at a short

notice (Ohannessian et al., 2020). In FRT, this arbitrage is a threat of a race to the bottom of

privacy norms, the derailment of rights which have been continually won on the international

arena.

The absence of a dedicated legal framework hinders the development of certified security

standards for FRT systems. As a result, these systems become prime targets for cyberattacks, and

the consequences for affected companies can be far more severe than any prior incidents

(Gladden et al. 2014; Coronado et al. 2011). However, as opposed to controlled sectors like

finance or healthcare, there is no mandatory, universal standard of cybersecurity-related storage

and processing of biometrics. This renders the giant reserves of facial pictures a loosely secured

and significant prize to malicious intent. The necessity of security frameworks established by the

government is not new in cybersecurity literature since uniform protocols are necessary to

safeguard critical information infrastructure (Srinivas et al., 2019). In addition to financial fraud,

the aftereffects of a breach go past a monetary loss as stolen biometrics are primary unalterable,

unlike a password or a credit card number, leaving a history of identity theft that the impacted

individuals will incur. Moreover, the uncontrollability hinders the creation of autonomous,

external control authorities that can audit the FRT systems in terms of adherence to ethical and

technical guidelines. In the absence of required disclosure and independent verification, it is

almost impossible to check statements made by the vendors into the correctness, equity, and

working boundaries of their technology. This is unlike other sensitive areas; in other cases,

environmental monitoring using drones can often demand environmental impact assessments and

operational approval (Vacca & Onishi, 2017).

The legal risks are aggravated by the technology that has the potential to leave an

irreversible fact on the ground that has normalized mass surveillance to even the extent of being

normalized before the people or the legislature can even debate on the subject. The instinctive

humility of FRT by years of usage might be able to make it the de facto regulation and any

endeavors to regulate it will be a formidable struggle against long time practices developed by

the commercial and government. This would be the same case with older surveillance

technologies in the sense that though they were infiltrated gradually into the lives of people they

slowly eroded the need of privacy even before the law could be informed. The necessity to

transition to telemedicine because of the COVID-19 pandemic, as urgent as it is, justified the

pace at which the healthcare delivery and patient needs can be transformed under the emergency

use determination, forcing the subsequent scramble to long-term regulation (Ohannessian et al.,

2020). In the case of FRT, this path dependency is a pop-up deadly threat and the societies are

bound to a future of surveillance not existing out of choice.

The inability to come up with custom designed FRT laws encourages the use of a

patchwork of old laws that introduce inconsistency in the law and makes fundamental rights

unsecured. The general data protection regulations, developed to address traditional personal

data have not been very well suited to address the peculiarities of biometrics, which are

identifiable by definition and permanently changed. Accordingly, stalking or harassment laws

cannot cope with the systemic surveillance of FRT, which is automated. Thanks to this jurist

defiant, it raises a question of ethically egregious uses of technology not being by the very reason

that the law does not see a safe harbor to abuse. The issue of regulation of blockchain, a non-

conforming technology to established legal groups as well, reflects the necessity to work out new

and innovative legal approaches to the problems rather than place the new technologies within

the old legal order (Batubara et al., 2018). Without an economic and active legal authority, the

rule of law will become ever more obsolete at the dynamics of digital existence.

Algorithmic Bias and Inaccuracies

The important aspect that allows for widespread use of technology is accuracy.

Technologies are adopted worldwide because it reduces human errors in accomplishing tasks,

which improves productivity and service delivery. However, Vaccan and Onishi (2017) indicate

that facial recognition technology is prone to inaccuracies. Even though it promises identification

accuracy, many studies show how its algorithms portrayed racial biases when identifying people

of color, especially its women (Vaccan & Onishi, 2017). The algorithmic biases and errors raise

concerns since it has brought about increase unlawful arrest of people of color (Phillips et al.,

2018). Also, it has increased the discrimination of people of color, especially their women, by

states, agencies, and organizations. Yet, they are a minority group that requires more protection

from such states, companies, agencies, and states (Khan et al., 2018). Therefore, such biases and

errors increase the plight of a minority group of people, aggravating their challenges in society.

Khan et al. (2018) state that such biases are problematic as they lead to bad decisions that should

be avoided in determining private corporations or government agencies’ treatment of individuals.

It is because it has the potential of causing unequal treatment leading to increased inequalities.

The primordial assurance of facial recognition technology is an automation of the object

of superhuman excellence in identifying. It is the pervasive algorithmic-bias that are

compromised with this promise that creates a new form of error and makes them system wide.

And these misrepresentations are not a coincidence but are embedded in the structure, and are

often an expression and even a magnification of historical discrimination in the society.

According to the argument posed by Brey, technology is not value-neutral and can persecute

some practices under the guise of technical neutrality, which creates a blank in the ethics of mass

surveillance in the community. Such a weakness is fundamentally manifested by FRT, in which

there is a significant performance gap among the demographics and which is a thoroughly

documented failure as it contradicts the basic assumptions of the concept of fairness and

reliability. These intrinsic issues turn what should be a security helping tool into a possible agent

of institutionalized oppression.

The first and most obvious cause of this systematic bias is the training data that is not

representative and, in many cases, homogeneous to formulate the involved complicated

algorithms. When an image based system is trained on more of the lighter-skinned male faces, it

inherently becomes very accurate on that particular group but disastrously ineffective in

recognizing faces on the less represented groups, especially darker skinned women.

Representational harm as suggested by researchers is one of the technical shortcomings in FRT

systems that render some groups more visible (Gikay 2023; Drachsler and Greller 2016). These

deficits also predispose the affected population to make sporadic mistakes and misunderstand the

purpose of automated systems use in sensitive settings. Moraes et al. (2021) also warn that the

implementation of such malfunctioning systems in semi-public places such as shopping centers

or transit stations is highly problematic, providing two-layered technological citizenship

experience. Within such a reality, the degree of protection, attention, and non-surveillance an

individual experiences is hinged not on his/her conduct, but rather how suited his/her physical

traits are in a property and in most cases non-transparent dataset.

The possible damages go beyond simple misidentification to the more far-fetched and

threatening area of affective computing whereby FRT is applied to draw conclusions about

emotional or psychological affections or traits based on facial expressions. This application

presents radical and new human rights issues, in that it is seeking to automate the interpretation

of complex, culturally neutral, and extremely variant human behaviors, which is a subjective task

subject to numerous variables. Neroni Rezende (2022) examines the idea that applying this kind

of technology on what is termed as preventive maintenance, e.g. algorithmically identifying

people who are considered to be aggressive or deceivers due to unsupported biometric

correlations, is not founded on a sound scientific basis. This habit exposes a person unfairly to

punishment because they are expressive by nature or circumstance. As Fontes and Perrone

(2021) note, is ethically dangerous because the guesses made in this direction are the ones that

move beyond identifying the behavior of known people to conjecture about the future behavior

of their potentially unknown peers. It is a major increase in the powers of surveillance, which

pose a huge concern in abuse and exploitation. Communities that are vulnerable are the most

impacted as their patterns can be misunderstood or pathologized by biased or prejudiced

computational systems.

Integrating such inherently faulty and biased systems into high-stakes institutions such as

schools is especially concerning, because it naturalizes the high-order of surveillance at a tender

age and poses the risk of instilling inequity into the learning process. The application of FRT in

the sphere of education is subjected to critical concerns as highlighted by Andrejevic and Selwyn

(2020), whose examples also imply methods of monitoring the attendance of the students, not to

mention the analysis of their behavior and interest based on the capacity of the automatic

analysis of sentiments. They caution that this breeds an oppressive aura of surveillance which

inhibits the open and trusting environment which is a prerequisite to learning. In the case of

educational technologies, system inaccuracies can add algorithmic errors to the records of

students, and this may result in false labeling. Repetitive exposure to inadequate systems can also

lead to the misinterpretation of the technological justice in the eyes of students so that errors in

systems are considered as normal.

The consequences of these registered inaccuracies to the whole society go way beyond

the instant of the recognition of the misidentification, and they impact the very essence of law

and democracy as a whole, undermining the principle of presumption of innocence in the open

places. Gentzel (2021), with passion, states the fact that biased FRT presents an independent and

extreme danger to the principles of liberal democracy and that the institution of biased FRT

constructs a de facto system of wherein members of demographics historically over-policed and

marginalized are automatically considered more likely to become suspects solely by entering a

monitored area. Whenever a technology is publicly known to be less precise with some racial or

gender groups every single interaction with it, at an airport, a protest, or a retail store, is already

predetermined by a increased and disproportionately high possibility of mistake. According to

Stajic (2015), the given technological reality effectively turns the space of the population to

become an experience of fundamentally unequal experience, in which the freedom to move and

meet without being falsely labeled through the algorithm will become a privilege not available to

all citizens, and thus will poison the trust and equality of each other that is in the healthy

functioning of the democratic society.

Moreover, the opaque and convoluted black box nature of most sophisticated algorithms

presents a distinct obstacle to mitigation and redress because it is hardly possible to trace the

error to a wrongly identified individual to comprehend the decision made by the system. As

Solarova et al. (2023) note, the extreme obstacle to it manifested in the absence of transparency

and clearness in the manner in which these systems accede to a certain identification causes a

considerable and sometimes insurmountable difficulty in achieving justice. The wrongly

identified individuals, who may have been connected to the crime they did not commit, do not

have clear means of appealing. Machines do not usually have a clear explanation and the affected

people have no viable way of appealing or seeking redress. According to Gikay (2023), such

technical opacities require a healthy and progressive regulatory reaction that goes beyond

stipulating performance thresholds. Algorithms should be audited by independent parties and

accessible channels of redress should be created, which is to be done properly. Those need to

have the opportunity to dispute poor choices made by algorithms and the system should maintain

control over the procedural and legal activity.

Conclusion

In conclusion, this essay discussed the arguments for and against facial recognition

technology in public space. It indicated that even though facial recognition technology has

numerous advantages, it also has numerous disadvantages. Proponents of facial recognition

technology say it can prove successful in preventing identity theft and fraud. For instance, in the

UK, Facewatch Company indicates that it has been able to stop over 217 fraudulent identity theft

cases. Also, it has allowed police authorities to identify criminals in the public space leading to

the reduction of crime rates. It has also led to the development of digital identity and scoring

systems, becoming the most fundamental ways governments, agencies, organizations, and

companies worldwide operate with ease by creating individual profiles and single identification

cards. It could be used to enhance public health protection measures. It is accomplished by

monitoring compliance with the need to use facial masks in public space, keeping social

distance, and compliance with quarantine through the installation of cameras in such space.

However, arguments against its use in public space indicate that it leads to collecting and storing

personal health information in a single database without their full and informed consent. It leads

to blanket surveillance by the government, which violates human rights. There is no clear

regulatory framework leading to increased abuse. It is open to inaccuracies based on its bias in

identifying people of color, leading to concerns about racial discrimination.

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