What is the Role of Facial Recognition in Predictive Policing?

Trust is money in today’s digital-first economy. Police departments all over the world are implementing technology-driven approaches to crime prevention as a result of cities becoming more digital and threats changing. Two of them, specifically predictive policing systems and facial recognition systems, are revolutionizing policing today by vowing to predict criminal activity and identify suspects in real time.

According to a Market us report on AI in Predictive Policing, the market is expected to grow at a compound annual growth rate (CAGR) of approximately 46.7% from USD 3.4 billion in 2024 to USD 157 billion by 2034. These statistics reveal a growing reliance of governments and security agencies on AI-led surveillance to prevent crime and speed up crisis response time.

But these technologies also have profound ethical and legal issues that give rise to certain questions. Are these technologies efficient in crime prevention, or are they perpetuating previous prejudices using new technology?  In what ways might facial recognition technologies be applied to all stakeholders’ concerns while optimising public safety? What rules govern the responsible use of these systems, and what effect do they have on civil liberties? And what are the laws regarding their employment? These critical questions will be discussed in detail throughout the blog

What is Predictive Policing?

Predictive policing is the application of data analysis and machine learning algorithms to predict the locations of imminent crime or pinpoint individuals who statistically have a likelihood of committing it. These programs look at historical crime patterns, such as the nature of the crime, time, and location, and use them to generate risk assessments and hotspot maps.

Facial recognition technology is increasingly being incorporated into predictive policing systems, allowing law enforcement to instantly identify known criminals or people of interest. By comparing faces recorded on cameras with criminal databases, this combination improves surveillance accuracy and expedites investigations.

For instance, if a community has had past instances of car thefts at night on weekends, the system can mark it as a high-risk area for future thefts. The police can then actively send officers to patrol in this area, hopefully preventing or even intercepting criminal activity before it spreads.

Predictive Policing and Facial Recognition 

When used in conjunction, predictive policing and facial recognition constitute a pre-emptive surveillance structure. Predictive analytics mark neighborhoods or individuals, and facial recognition technology tracks those marked in real time. This produces a feedback loop where suspicion generates more suspicion.  

For example, if predictive analytics has flagged a specific urban area as having high crime levels, individuals could be arrested, interrogated, or profiled for years without having committed any offense. 

This type of policing is frequently criticised and has numerous shortcomings. It typically affects already vulnerable communities by blurring the distinction between preventing crime and suspecting individuals.

Legal and Ethical Concerns

Due to alleged ethical and legal issues, the use of facial recognition and predictive policing technologies for criminal profiling and surveillance has faced some backlash. Despite being innovative, these technologies are not immune to systemic problems.

There are three main issues discussed in detail as follows : 

• The Black Box Issue

Law enforcement frequently lacks clarity on how facial recognition and predictive policing systems make decisions, which results in them drawing ill-informed conclusions. Many of these systems function as “black-box”.

• Data That Discriminates: Built-in Bias

Police predictive software is trained on historical police data, which tends to reflect a bias toward over-policing low-income or minority communities.

In Oakland, California, a Human Rights Data Analysis Group study discovered that Black residents were disproportionately more likely to be stopped and searched. Inputting this discriminatory data into predictive systems resulted in repeated targeting of those same populations, regardless of crime rates. This results in a self-perpetuating cycle where surveillance breeds suspicion, not security.

• Pre-Crime or Profiling?

When law enforcement begins acting on predictions and face matches rather than concrete actions, society can slide toward a regime of digital profiling.

This is evident from the example of the Chicago Strategic Subject List (SSL); the list included hundreds of individuals without records who were identified as high-risk. According to a predictive algorithm, they were then monitored or questioned. Reducing statistical probabilities to justification for surveillance or detention undermines civil liberties and drives policing toward population control, not crime solving. 

For these concerns, there are several legislations made to mitigate the rising threats of it.

Legal Environment: International Policies and Safeguards

• As facial recognition and predictive policing start becoming mainstream, international regulations tighten scrutiny to balance security and civil liberties. Here’s a brief overview of how major regions are regulating these technologies.
• The EU AI Act, set to become fully applicable by mid-2026, prohibits real-time biometric monitoring in public places unless indispensable and authorized by judicial powers. 
• The General Data Protection Regulation (GDPR) also categorizes facial data as sensitive data, and hence, explicit consent and limitation of purpose are necessary.
• The Illinois Biometric Information Privacy Act (BIPA) requires explicit consent for the use of biometric information.
  

Using Technology for Predictive Policing More Wisely and Equitably 

Experts from international organizations such as INTERPOL and the World Economic Forum concur that predictive policing and facial recognition need to be applied with restraint and transparent rules.

The following are what responsible use would entail:

• Communicate openly with People: 

Inform the public what technology is being applied, where it’s applied, and why. When they know they’re being scanned or tracked, they can ask questions and make themselves informed. Concealing it only causes fear and mistrust.

• Maintain Human Authority:

AI is a valuable tool to aid in investigations, but only human authorities should make decisions, particularly in arrest decisions.  Governments in cities and law enforcement bodies need to approach AI-created analysis with a critical eye before making any moves.

• Check Frequently for Fairness:

We must ensure that the system is not targeting particular groups. Regular testing will reveal if it’s unfair and, if so, it needs to be corrected. No technology should discriminate against people based on their appearance or where they reside.

• Avoid Storing Face Data For Longer Periods of Time

Facial recognition data should be deleted after a short time, especially if no crime has occurred. Keeping it too long increases the chances of misuse or data leaks. 

Many city governments have used a lot of public input to decide how to use facial recognition and other surveillance technologies in the past. For example, in 2020, the Baltimore city government made it a policy to consult with the public before deploying facial recognition. They had open meetings and heard the people before deciding, demonstrating how transparency and citizen input can safeguard rights and yet enable intelligent policing.

Facia’s Visions: Bias-Free and Collaborative Face Recognition 

Facial recognition and predictive policing hold the power to redefine the prevention of crime only if they are used with precision, ethics, and public oversight. Abused or left unchecked, these technologies can reinforce intrusion on privacy and decay public trust. But when responsibly built, these can enhance public safety without giving up individual rights. That’s where Facia is creating a new standard. 

• Facia actively collaborates with governmental bodies and community groups to promote the open application of facial recognition technology, supported by accountability, public consultation, and integrated securty measures. From precise identity verification to unbiased authentication, Facia provides tech as responsible as it is revolutionary. 
• Facia’s approach to fairness and diversity helps combat these issues by ensuring algorithmic equity across demographics. Since law enforcement databases serve as the foundation for confirming suspects’ identities, their quality and accuracy are essential in 1:N matching. 
• Facia’s innovative facial recognition SDKs, paired with 3D liveness detection and privacy-first design, allow law enforcement to:
• Securely authenticate personnel and detainees in buildings and courtrooms, 
• Prevent deepfake spoofing and incorrect identifications through strong biometric verification, 
• Comply with international legal systems such as GDPR and BIPA.
• Facia enables public safety efforts with real-time accuracy and multi-ethnic fairness. With security and morals being required to walk hand in hand in this day and age, Facia provides the trust layer that facial recognition has lacked for so long. 
• Facia provides predictive policing technology that strikes a balance between safety, accountability, and smooth governance by collaborating closely with law enforcement and local authorities.