Leveraging Technology

Leveraging Technology: Bridging the Data Gap with Missing Persons and Human Trafficking

Author: Melody Peace

October 17, 2025

Human trafficking and missing persons cases are inherently complex and time-sensitive. Yet, fragmented reporting and frequent changes in data policies make it difficult to measure progress accurately. These inconsistencies obscure the true scope of the problem and hinder effective resource allocation. Compounding this challenge, current AI applications often prioritize efficiency in reporting rather than focusing on solving cases. By shifting AI innovation toward actionable solutions, tools that help detect, coordinate, and prevent incidents, investigators can achieve more meaningful outcomes and respond more effectively.

Between 2005 and 2024, millions of missing person cases were recorded in the FBI’s National Crime Information Center (NCIC). At first glance, the numbers appear to show improvement: in 2006, there were 836,131 entries with 96,183 active cases at year’s end (Federal Bureau of Investigation, 2007). By 2024, annual entries had fallen to 533,936, with 93,447 active cases (Office of Juvenile Justice and Delinquency Prevention & FBI, 2024). However, these figures do not tell the full story. Changes in reporting procedures, policy updates, and data integration efforts have redistributed cases across multiple systems rather than eliminated them. The apparent decline reflects administrative evolution, not a straightforward reduction in missing persons.

Several major milestones shaped missing person reporting during this period. Suzanne’s Law (2003) required mandatory reporting for individuals under 21, immediately increasing case counts (FBI, 2007). The National Missing and Unidentified Persons System (NamUs) launched its Unidentified Persons Database in 2007 and its Missing Persons Database in 2008, followed by automated matching in 2009. NamUs 2.0 in 2018 modernized cross-agency integration, and legislation like Savanna’s Act and the Not Invisible Act (2020) enhanced accountability for Indigenous populations. These procedural shifts underscore an important point: fluctuations in reported cases often reflect evolving definitions, reporting obligations, and inter-agency coordination, rather than actual changes in the number of missing persons.

Understanding these contextual factors is essential when considering how technology can support investigations. Tools like AI, data integration, and predictive analytics can fill the gaps created by inconsistent records, but only if the underlying data is accurate, standardized, and ethically managed. Otherwise, automated systems risk reflecting administrative quirks instead of real-world conditions. Conceptually, a modern framework would integrate anonymized data from law enforcement, NGOs, shelters, social media, CCTV, telecom metadata, and open-source intelligence. This information would be de-identified, mapped to standardized schemas, and stored securely with clear consent metadata.

Advanced analytics, including natural language processing, risk scoring, anomaly detection, facial and object recognition, and geospatial-temporal clustering, can help identify patterns, hotspots, and high-priority leads. Decision-support dashboards would visualize these findings with ranked leads, heatmaps, timelines, and relational graphs. Human investigators would validate leads, coordinate field operations, and provide feedback to retrain AI models, ensuring that automated outputs complement, rather than replace, expert judgment. Governance protocols, including access logs, ethical oversight, and explainability reports, maintain accountability throughout the process.

In today’s world, cameras and sensors are everywhere, from smartphones and webcams to traffic lights, security systems, and social media platforms. Almost every public and semi-public space is under some form of recording, creating a massive, untapped network of observational data. By integrating this existing technology into a coordinated system for public safety, law enforcement could receive real-time alerts about missing persons or potential trafficking situations without ever revealing the identity of individuals until verification is complete. This approach leverages devices and networks already in place, turning passive recordings into actionable intelligence while protecting privacy and minimizing additional costs. The result is a smarter, faster, and more proactive way to respond to cases before they escalate.

Private companies are increasingly leveraging facial recognition technology (FRT) to enhance security, streamline operations, and boost profitability. The global facial recognition market was valued at approximately $8.09 billion in 2024 and is projected to reach around $32.53 billion by 2034, growing at a compound annual growth rate (CAGR) of 14.93% during the forecast period Precedence Research. This rapid growth underscores the increasing adoption of FRT across various sectors, including retail, healthcare, and finance.

In the retail industry, for instance, companies like Wesfarmers are integrating FRT to combat rising retail crime. The company's CEO has advocated for the responsible use of facial recognition technology to focus on known offenders, aiming to enhance security and reduce losses. Similarly, AI-powered surveillance systems, such as those developed by Veesion, are being employed in over 4,000 stores across 25 countries to significantly reduce shoplifting incidents Safe and Sound Security.

These implementations not only bolster security but also contribute to cost savings by reducing theft-related losses and administrative burdens. As the market continues to expand, the financial benefits for private companies utilizing FRT are becoming increasingly evident.

Implementing continuous monitoring for missing person and victims of human trafficking would begin with partnerships, legal agreements, and the establishment of ethical oversight committees. Secure infrastructure with role-based access controls would be deployed, and investigators trained on standardized workflows and escalation procedures. The system could be piloted in select regions for three to six months, comparing system-assisted leads to conventional investigative approaches. A phased scale-up would integrate cross-agency operations using standardized APIs. Legislative and incentive strategies could encourage private-sector participation: limiting social media platforms, gaming apps, webcam services, and mobile device manufacturers explicit use of facial recognition or AI for revenue in exchange for continuous monitoring for missing persons and victims of human trafficking. Embedded point-of-capture alert systems could notify law enforcement immediately when a potential missing person is detected, and secure, anonymized data sharing would protect privacy and civil liberties. Incentives like tax credits, recognition programs, or liability protections could further encourage compliance.

If implemented effectively, this approach could reduce time-to-identification for missing persons, increase investigator efficiency, and improve detection of non-obvious patterns while maintaining low false-positive rates. Limitations remain, including legal constraints on data coverage, potential issues with model generalizability across jurisdictions, the need to protect civil liberties, and reliance on the quality and coverage of input data such as CCTV and social media activity. Nonetheless, by combining data-driven analysis, advanced technology, and thoughtful policy incentives, the system offers a proactive path toward more effective, coordinated responses to human trafficking and missing persons cases.

References:

Business Insider. (2025). CEO of Clearview AI, the startup that scraped billions of online face images, resigns. Retrieved from https://www.businessinsider.com/clearview-ai-ceo-resigns-hal-lambert-richard-schwartz-2025-2
Reports on the leadership change at Clearview AI and its impact on the company’s operations in the facial recognition market.

Federal Bureau of Investigation. (2007). National Crime Information Center (NCIC) 2006 Missing Person File Statistics. Retrieved from https://www.fbi.gov/file-repository/ncic-2006-missing-person-file-statistics.pdf/view
Provides NCIC missing person statistics for 2006, including total entries and active cases, and explains the impact of Suzanne’s Law on reporting.

National Institute of Justice. (2018). Upgrading the National Missing and Unidentified Persons System: Introducing NamUs 2.0. Retrieved from https://nij.ojp.gov/library/publications/upgrading-national-missing-and-unidentified-persons-system-introducing-namus-20
Discusses the launch and evolution of NamUs, its databases, and automated matching features, showing improvements in cross-agency data integration.

National Institute of Justice. (2019). Solving the Missing Indigenous Person Data Crisis: NamUs 2.0. Retrieved from https://nij.ojp.gov/topics/articles/solving-missing-indigenous-person-data-crisis-namus-20
Explains NamUs 2.0’s role in modernizing cross-agency integration and improving case tracking for Indigenous populations.

National Institute of Justice. (2025a). NamUs and DIAMD Integration Coming Soon. Retrieved from https://namus.nij.ojp.gov/namus-and-diamd-integration-coming-soon
Discusses planned integration of NamUs with DIAMD for enhanced data interoperability and analytics.

National Institute of Justice. (2025b). NamUs: Narrowing the Search for Missing Persons. Retrieved from https://domesticpreparedness.com/articles/namus-narrowing-the-search-for-missing-persons
Highlights the use of NamUs data and analytics to assist investigations and improve case resolution.

National Institute of Justice. (2025c). Introducing NamUs 2.0. Retrieved from https://namus.nij.ojp.gov/media/video/131
Provides an overview of NamUs 2.0 features, including modernized cross-agency integration and improved database management.

Office of Juvenile Justice and Delinquency Prevention & Federal Bureau of Investigation. (2024). Missing Person Entries and Active Cases: 2024 Report. Retrieved from https://ojjdp.ojp.gov/sites/g/files/xyckuh176/files/pubs/2024/missing-persons-report-2024.pdf
Provides NCIC statistics for 2024, showing trends in missing person entries and active cases, highlighting challenges in interpreting data over time.

Precedence Research. (2025). Facial Recognition Market Size and Forecast 2025 to 2034. Retrieved from https://www.precedenceresearch.com/facial-recognition-market
Provides projections for the global facial recognition market, highlighting significant growth from 2025 to 2034.

Safe and Sound. (2025). Facial Recognition Trends and Statistics. Retrieved from https://getsafeandsound.com/blog/facial-recognition-trends-and-statistics/
Discusses the widespread adoption of facial recognition technology in various industries and its implications for security and privacy.

U.S. Department of Justice. (2020a). Savanna’s Act. Retrieved from https://www.justice.gov/tribal/mmip/SavannasAct
Provides legislative context for enhanced reporting and data collection for missing Indigenous persons.

U.S. Department of Justice. (2020b). Not Invisible Act. Retrieved from https://www.justice.gov/tribal/not-invisible-act
Outlines legislation improving accountability and investigation of missing Indigenous individuals.

Career Progression in Law Enforcement

Career Progression in Law Enforcement: The Paramilitary Pyramid

Author: Melody Peace

September 30, 2025

Law enforcement agencies are often characterized as paramilitary organizations, emphasizing discipline, hierarchy, and accountability. However, this characterization is predominantly applicable to tactical operations and organizational structure. When examining career progression and promotion practices, the paramilitary analogy becomes less applicable. Evidence suggests that advancement within law enforcement agencies often mirrors a pyramid structure, where hierarchical mobility is constrained, creating conditions that incentivize compliance over merit.

At the foundational level, many officers enter the profession with professional aspirations and a commitment to public service. Yet, as rank ascends, opportunities for advancement narrow substantially, culminating in a singular apex position. This scarcity engenders a competitive environment in which personal relationships frequently supersede objective qualifications, and loyalty may be prioritized over demonstrated competence. Consequently, misconduct can function as a form of currency within the promotion system, whereby career advancement is contingent on adherence to informal or covert norms rather than measurable performance indicators.

In highly corrupt agencies, empirical observation indicates that promotion frequently requires demonstrable loyalty to the existing organizational culture. Officers are often compelled to demonstrate complicity in order to progress, including the concealment of misconduct, falsification of reports, or protection of supervisors from accountability. In this context, loyalty is evaluated less by tenure or professional expertise and more by willingness to compromise ethical standards. Conversely, officers who possess advanced certifications or extensive formal training may be perceived as a threat to entrenched organizational hierarchies, highlighting the tension between meritocratic principles and relationally driven promotion systems.

By contrast, military institutions maintain uniform standards for advancement. Promotion eligibility is contingent upon completion of standardized testing, fulfillment of training benchmarks, and demonstrated competence, rather than subjective evaluations or favoritism. Adoption of analogous systems in law enforcement could mitigate opportunities for corruption and align career advancement with objective performance measures.

Several structural reforms have been proposed to promote merit-based advancement in law enforcement:

• Implement time-in-grade, time-in-service, physical fitness, and aptitude requirements.

• Establish independent oversight boards to administer promotion decisions.

• Reinforce foundational hiring criteria at each stage of advancement, including drug screenings, polygraph examinations, background investigations, and supervisory references.

• Transition from subjective evaluations to fact-based, evidence-driven assessments.

Standardized progression criteria ensure that officers are equipped to assume higher responsibilities and reduce reliance on relational leverage in promotion decisions. Independent oversight serves to curtail the use of promotion as a mechanism of coercion or reward for complicity, thereby facilitating identification of qualified personnel. Consistent application of hiring and evaluation standards reinforces accountability and integrity, while fact-based assessment processes allow for differentiation between officers exhibiting merit and those enabling misconduct. Routine, objective queries provide a systematic approach for assessing ethical compliance and exposing corruption.

While standardized assessments and objective evaluations are critical, data alone can also illuminate the underlying sources of corruption within an agency. For instance, if an entire department uniformly denies the existence of misconduct, responses to questionnaires or surveys may be largely uninformative, signaling a culture of fear or collective silence rather than the absence of wrongdoing. In such cases, resources should be redirected to investigate potential reprisals or intimidation tactics that discourage officers from reporting violations. Polygraph examinations and questionnaires should be revised to identify not only offenders but also victims, acknowledging that officers who remain silent out of fear—whether for self-preservation or due to systemic pressure—are nonetheless compromised. Similarly, analyzing an officer’s involvement in illicit activity, such as drug use or trafficking, must be balanced with scrutiny of those who are too intimidated to report misconduct, as both forms of complicity undermine organizational integrity and can perpetuate cycles of corruption.

While hierarchical structures are an inherent feature of law enforcement organizations, misconduct need not be embedded in the promotion process. By anchoring advancement in verifiable performance metrics, independent review, and standardized evaluation, agencies can diminish the currency of complicity and foster meritocratic progression.

To achieve sustainable reform, law enforcement agencies must adopt transparent, standardized promotion practices. Policymakers, oversight entities, and professional stakeholders should advocate for evaluative frameworks that emphasize measurable criteria, independent adjudication, and objective performance assessment. Without systemic reform, silence and complicity will continue to be rewarded, while skill and integrity are devalued. Deliberate restructuring that prioritizes merit and accountability is essential to realign career progression with ethical and professional standards, thereby restoring organizational legitimacy and public trust.

References

1. Singh, D. (2022). The Causes of Police Corruption and Working Towards Prevention Strategies. Laws, 11(5), 69. https://doi.org/10.3390/laws11050069

   This article examines the causes and consequences of police corruption, particularly in volatile environments. It highlights factors such as low pay, nepotism, and weak performance evaluation systems as contributors to misconduct.

2. Lado, R. N., Prasojo, E., & Jannah, L. M. (2025). Institutional Barriers to Merit-Based Career Development in the Police: A Review of Global and Local Perspectives. https://www.researchgate.net/publication/392437646

   A systematic review of 50 peer-reviewed articles identifies barriers to implementing merit-based career development systems in police organizations, including rigid bureaucratic hierarchies and cultures of patronage.

3. Cruickshank, D. (2013). Evaluating the Paramilitary Structure and Morale. FBI Law Enforcement Bulletin. https://leb.fbi.gov/articles/perspective/perspective-evaluating-the-paramilitary-structure-and-morale

   This perspective discusses the paramilitary model in law enforcement, noting that while it provides command and control, it may also impact morale and adaptability

4. Taylor, O. E. V. (2024). Police Whistleblowing: A Systematic Review. Journal of Criminal Justice, 91, 101-112. https://doi.org/10.1016/j.jcrimjus.2024.101112

   This review identifies barriers and facilitators to officers challenging misconduct, emphasizing the need for supportive structures to encourage ethical behavior.

5. Savery, L. K. (2025). Merit-Based Promotion Systems: Police Officers' Views. https://search.informit.org/doi/10.3316/cinch.208322

   An examination of police officers' perceptions of merit-based promotion systems, highlighting advantages and disadvantages compared to seniority-based systems.

6. Lothian, R. A. (1957). Operation of a Police Merit System. Journal of Public Administration, 37(2), 97-106. https://www.jstor.org/stable/1030343

   A study on the implementation of a police merit system, discussing the development of examination announcements and the importance of transparency in the promotion process.

7. Perkins, C. A. (2023). Law Enforcement Leadership and Organizational Culture in a Post-2020 Society. Marshall University Theses, Dissertations, and Capstones. https://mds.marshall.edu/etd/1820/

   This research explores the impact of organizational culture on leadership within law enforcement, noting that a paramilitary rank structure can influence leadership styles and effectiveness.

8. Geneva Centre for Security Sector Governance (DCAF). (2025). Corruption Control and Integrity-Building in Law Enforcement. https://www.dcaf.ch/corruption-control-and-integrity-building-law-enforcement

   A report examining systemic reforms and practical interventions designed to promote transparency, accountability, and professionalism within law enforcement agencies.

Metadata in Digital Forensics

Metadata in Digital Forensics: Clues and Conundrums

By Melody Peace

September 29, 2025

Digital images have become a crucial tool in modern law enforcement investigations, largely due to geotagging—the process of embedding GPS coordinates in photos. When a photo is taken with a smartphone or GPS-enabled camera, location data is often stored within the image’s EXIF metadata, along with information such as date, time, and camera details (Singh, 2019; Magnet Forensics, 2024). This metadata allows authorities to pinpoint where and when a photo was taken, providing vital clues in criminal investigations. For example, police have used geotagged social media images to track suspects, confirm alibis, or locate missing persons (Magnet Forensics, 2024).

Geotags have proven particularly valuable in cybercrime, theft, and violent crime cases. Images posted online often reveal locations that perpetrators did not intend to disclose. Law enforcement agencies can extract this data to cross-reference suspect movements with crime scenes, narrowing down investigations and even leading to arrests (Soni, 2025).

An important consideration in digital forensics is the role of screenshots. When a photo is screenshotted, the new image usually does not retain the original photo’s EXIF metadata, including geotags. The screenshot only records new metadata, such as the date and time of the screenshot and the device used (Singh, 2019). Therefore, if a suspect deletes the original image, investigators cannot rely on geotags from the screenshot itself. This distinction emphasizes the need for quick preservation of original digital evidence. Further innovation that allows the original metadata to be preserved while rewriting visual content during a screenshot would prove highly useful, enabling investigators to retain critical location and timestamp information even when images are duplicated or modified (Magnet Forensics, 2024).

Despite this limitation, geotags remain a powerful tool. Law enforcement agencies often educate the public on the risks of sharing images online with embedded location data. Users can remove geotags manually or adjust device settings to prevent location data from being stored, but failure to do so can inadvertently reveal critical information to investigators (Magnet Forensics, 2024).

The use of social media metadata in criminal investigations raises important legal questions. While publicly shared information on platforms such as Instagram, TikTok, and Facebook is often accessible to law enforcement, prosecutors, and private investigators without a warrant, this practice can infringe on individuals' privacy rights. In California, for example, courts have ruled that social media posts can be used as evidence in criminal cases, but the legality of accessing this data without consent remains a contentious issue (LA Criminal Defense Lawyer, 2025).

Conversely, the introduction of deepfake evidence in legal proceedings necessitates stringent authentication procedures. For instance, the proposed Federal Rule of Evidence 901(c) addresses the authentication of deepfake evidence, emphasizing the need for courts to establish the genuineness of digital content before admitting it as evidence (Library of Law, UIC, 2025).

While social media metadata can aid in solving crimes, the rise of deepfake technology presents new challenges. Deepfakes—AI-generated audio, video, or images that manipulate real content—can be used to fabricate evidence, create false alibis, or discredit individuals (HaystackID, 2025).

A sophisticated deepfake scam in Hong Kong involved AI-generated video calls where fraudsters impersonated company executives to authorize financial transactions. The perpetrators managed to steal $25 million before the fraud was detected. This incident underscores the significant risks posed by deepfakes in financial and corporate settings (CoverLink Insurance, 2025).

In an era dominated by social media and digital imagery, a critical question arises: should the context of an image justify subpoenaing its metadata? While visual content can suggest locations, timelines, or participants in potential criminal activity, investigators must carefully evaluate whether these cues meet the legal threshold for probable cause. Complicating matters further is the rise of deepfakes and hyper-realistic digital entertainment, which can make distinguishing genuine forensic evidence from manipulated or fictional content increasingly challenging. This tension highlights the need for rigorous verification methods and legal safeguards to ensure that metadata is only accessed when it can be reliably tied to actual criminal activity prior to its acquisition (Axios, 2025).

Geotags and image metadata have undeniably transformed modern investigations, offering unprecedented insight into locations, timelines, and suspect behavior (Soni, 2025). Yet, as technology evolves, so do the questions surrounding its use. If screenshots strip metadata and deepfakes can fabricate reality, how can investigators be certain that digital evidence reflects the truth? Does the context of an image alone justify accessing its metadata, or must courts rethink standards for probable cause in the digital age? As AI-generated content becomes indistinguishable from reality, who decides what constitutes credible evidence, and what safeguards should exist to prevent abuse? Will the very technologies designed to solve crimes one day create more uncertainty than clarity? In this rapidly evolving digital landscape, digital forensics could redefine the future of forensic science. As social media continues to blur the lines between personal expression and forensic evidence, society must grapple with how to balance privacy, innovation, and justice. Are we equipped to distinguish between genuine evidence and sophisticated digital fabrications, or will the tools meant to solve crimes become weapons of misdirection?

References

1. CaseGuard. (2019). Digital Evidence, EXIF Data, and Law Enforcement Agencies.
   This article discusses how EXIF metadata can serve as a secondary layer of data in digital evidence, aiding in pinpointing exact information concerning a crime and assisting in eliminating suspects. CaseGuard

2. Magnet Forensics. (2024). Not All Geolocation Data Is Created Equal.
   Explores the forensic examination of mobile digital devices, highlighting the variety of GPS-source information that can be extracted to reconstruct crime scenes, establish timelines, and verify alibis. Magnet Forensics

3. Soni, N. (2025). Forensic Value of EXIF Data: An Analytical Evaluation.
   This research assesses the integrity of EXIF information across various methods of image transmission, such as USB, email, and messaging platforms, providing insights into the reliability of metadata in different contexts. SCIEPublish

4. Magnet Forensics. (2025). The “Deepfake Detector” Paradigm Shift: The Case for Media Authentication in Court.
   Highlights the shortcomings of current deepfake detection tools, emphasizing the need for media authentication to verify the authenticity of digital evidence in legal proceedings. Magnet Forensics

5. Illinois State Bar Association. (2025). Deepfakes in the Courtroom: Problems and Solutions.
   Discusses the challenges courts face in ascertaining the authenticity of digital evidence due to deepfakes, and the necessity for advanced forensic tools to verify authenticity. Illinois State Bar Association

6. U.S. Courts. (2025). DEEPFAKES ON TRIAL 2.0: A REVISED PROPOSAL FOR RULE 901.
   Proposes an amendment to Rule 901 specifically addressing deepfakes, clarifying the burden of proof for evidence suspected of being altered or fabricated by AI. United States Courts

7. Axios. (2025). Courts Aren't Ready for AI-Generated Evidence.
   Examines the preparedness of courts to handle AI-generated evidence, highlighting the challenges in verifying the authenticity of digital media and the inadequacy of current forensic tools. Axios

8. HaystackID. (2025). Inside the Deepfake Arms Race: Can Digital Forensics Investigators Keep Up?.
   Analyzes the evolving nature of deepfakes and the corresponding challenges faced by digital forensics investigators in detecting and authenticating synthetic media. HaystackID

The Ethical and Legal Implications of Facial Recognition Technology

The Ethical and Legal Implications of Facial Recognition Technology

By Melody Peace

September 29, 2025

Facial recognition technology (FRT) has become increasingly prevalent in various sectors, including entertainment venues, raising significant concerns about privacy, consent, and constitutional rights. One of the primary concerns with FRT is the collection of biometric data without explicit consent. Unlike traditional forms of identification, such as fingerprints, facial scans can be captured remotely and without an individual's knowledge, posing challenges to privacy rights. The lack of transparency in data collection processes exacerbates these concerns, as individuals may be unaware that their biometric data is being collected and stored (ISACA, 2021).

The application of FRT intersects with constitutional protections, particularly the Fourth Amendment, which guards against unreasonable searches and seizures. In Katz v. United States (1967), the Supreme Court held that the Fourth Amendment protects individuals' reasonable expectations of privacy, even in public spaces (Katz v. United States, 1967). This precedent suggests that the use of FRT without consent may constitute an unreasonable search, potentially violating constitutional rights.

The Court’s decision in Katz emphasized that the Fourth Amendment "protects people, not places," establishing that privacy expectations are not solely tied to physical locations but also to the nature of the information being collected. This principle has been applied in subsequent cases involving digital data collection and surveillance technologies. For instance, in Carpenter v. United States (2018), the Court extended Fourth Amendment protections to include historical cell phone location data, recognizing that individuals have a reasonable expectation of privacy in the digital information they generate (Carpenter v. United States, 2018). This expansion underscores the need for legal frameworks to adapt to technological advancements and protect individual privacy rights.

In the context of FRT, the continuous and often covert collection of biometric data raises significant privacy concerns. Unlike traditional forms of identification, facial recognition can occur without an individual's knowledge or consent, potentially infringing upon their reasonable expectation of privacy. The lack of clear regulations governing the use of FRT further complicates the issue, highlighting the necessity for updated legal standards that address the unique challenges posed by modern surveillance technologies (ACLU of Illinois, 2021).

Therefore, the implementation of FRT without explicit consent may violate constitutional rights by infringing upon individuals' reasonable expectations of privacy. Legal precedents such as Katz and Carpenter provide a framework for evaluating the constitutionality of surveillance practices, emphasizing the importance of protecting privacy in the face of evolving technologies.

Furthermore, the absence of comprehensive federal legislation regulating biometric data collection leaves individuals vulnerable to privacy violations. While some states, such as Illinois, have enacted laws like the Biometric Information Privacy Act (BIPA), which mandates consent and transparency in data collection, these laws are not uniform across the country (ACLU of Illinois, 2021). The lack of a federal standard creates a fragmented legal landscape, complicating the protection of individuals' biometric data.

Another critical issue with FRT is its potential for racial and gender bias. Studies have shown that FRT systems often exhibit higher error rates for people of color and women, leading to misidentifications and wrongful arrests (The Regulatory Review, 2020). These disparities highlight the need for rigorous testing and regulation to ensure that FRT systems are accurate and equitable.

The commercialization of biometric data adds another layer of concern. Companies may collect biometric data under the guise of enhancing customer experience, only to monetize this data by selling it to third parties, including government agencies. This practice raises ethical questions about consent and the potential for surveillance capitalism, where individuals' personal data is commodified without their informed consent (HRRC, 2022).

To address these concerns, experts advocate for the development of comprehensive federal legislation that establishes clear guidelines for the collection, use, and storage of biometric data. Such legislation should prioritize transparency, informed consent, and the protection of individuals' privacy rights. Additionally, independent oversight and regular audits of FRT systems can help ensure accountability and prevent misuse (ISACA, 2021).

While facial recognition technology offers potential benefits in terms of security and convenience, its implementation must be carefully regulated to protect individuals' privacy and constitutional rights. Without clear legal frameworks and safeguards, the widespread use of FRT poses significant risks to civil liberties. As technology continues to advance, it is crucial that legal protections evolve to keep pace, ensuring that individuals' rights are upheld in the face of emerging surveillance capabilities.

References

1. ISACA. (2021). Biometric Security and Privacy Considerations.
   Discusses privacy risks, transparency issues, and ethical concerns in the collection and storage of biometric data, including facial recognition. https://www.isaca.org/resources

2. Katz v. United States, 389 U.S. 347 (1967).
   Supreme Court case establishing that the Fourth Amendment protects individuals' reasonable expectations of privacy, even in public spaces; foundational for evaluating FRT legality. https://supreme.justia.com/cases/federal/us/389/347/

3. Carpenter v. United States, 585 U.S. ___ (2018).
   Extends Fourth Amendment protections to digital data, specifically historical cell phone location information, emphasizing privacy expectations in the digital age. https://www.oyez.org/cases/2017/16-402

4. ACLU of Illinois. (2021). Biometric Information Privacy Act (BIPA) Overview.
   Provides information on state-level legislation requiring consent and transparency for biometric data collection, highlighting gaps in federal regulation. https://www.aclu-il.org/en/cases/biometric-information-privacy-act-bipa

5. The Regulatory Review. (2020). Facial Recognition and Algorithmic Bias.
   Discusses evidence of racial and gender disparities in FRT systems and emphasizes the need for equitable design and testing. https://www.theregreview.org/2020/10/05/facial-recognition-bias

6. HRRC. (2022). Surveillance Capitalism and Biometric Data.
   Explores the ethical implications of commercial collection and monetization of biometric data, including potential misuse and consent issues. https://hrrc.org/surveillance-capitalism

Predictive AI in Law Enforcement

Predictive AI in Law Enforcement: Failing Communities While Misallocating Resources

By Melody Peace

September 29, 2025

Artificial intelligence (AI) is increasingly being integrated into law enforcement, particularly through predictive policing tools designed to forecast where criminal activity might occur. While intended to enhance public safety, current implementations of predictive AI often misdirect resources, inflate crime statistics, and fail to reduce actual crime.

The Promise—and the Reality—of Predictive AI

Predictive policing offers several potential advantages in theory:

• Efficient Resource Allocation: AI can analyze crime patterns to suggest where officers should patrol (Police Chief Magazine).

• Proactive Crime Prevention: Forecasting “hotspots” aims to allow police to intervene before crimes occur (SmartDev).

Yet, the most valuable evidence of predictive AI’s failure is its inability to reduce crime rates. While Law Enforcement Agencies over-police communities designated by AI, often those historically targeted through bias policing—crime statistics become artificially inflated. At the same time, actual criminal activity often goes unnoticed in under-patrolled areas, as officers, under the pressure of public scrutiny or political agendas, divert resources elsewhere. This combination of over-policing and unsolved crimes disproportionately inflates crime statistics into astronomical numbers, creating the illusion that crime is worsening. In reality, crime itself is not increasing; the perception of rising crime is largely a product of our over-reliance of predictive AI software designed built on statistics obtained from bias policing.

The Human Cost

Predictive AI in Law Enforcement at its current state perpetuates systemic inequities. The result: innocent civilians are profiled, and every encounter carries a heightened risk of escalation, with officers feeling the need to justify their actions during every encounter with use of excessive force.

Meanwhile, other areas with legitimate need for policing experience delayed response times, fewer patrols, and inadequate investigative attention, allowing crime to go unresolved. From both an ethical and operational perspective, this represents a major failure of current AI implementation.

Economic and Resource Implications

Financially, predictive AI wastes an incomparable number of resources. Law Enforcement Agencies relying on predictive software risk investing into areas that are historically overpoliced, while the need for real investigative resources compounds in neglected areas. The inefficiency most often results in supplemental allocation of funds, exacerbating the fiscal budget and public mistrust. The reallocation of funds from critical community development programs such as education and social services towards Law Enforcement agencies only intensifies existing inequities. The diversion of resources from essential programs also negatively affects crime rates used in the framework of predictive AI software, underscoring the urgent need to implement predictive policing strategies that are both effective and equitable.

Practical Solution: Altering the Framework of Predictive AI

Exploring a few actionable steps could begin to address these problems:

• Focus on financial loss: Prioritize resources in areas where unchecked crime or delays in response result in the greatest economic loss.

• Restructure Predictive Data: Use ethically sourced, representative data rather than relying solely on historical crime statistics from over-policed neighborhoods.

• Redesign Patrol Allocation: Base patrols on emergency call patterns rather than algorithmic “hotspots” alone.

• Increase Presence in Surveillance Deserts: Expand visibility in areas traditionally under-monitored to ensure actual crime is identified and investigated.

Implementing effective reforms allows predictive AI to allocate resources effectively toward improving public safety without perpetuating inequity or unnecessarily wasting taxpayer dollars.

Moving Forward: Research and Accountability

To ensure predictive AI is used responsibly, agencies must invest in further research, oversight, and equity-focused protocols:

• Independent Audits: Regular review of AI systems for bias, fairness, and effectiveness.

• Transparency: Public access to algorithms and datasets to allow scrutiny.

• Community Engagement: Involving local stakeholders in AI deployment ensures alignment with public needs and values.

• Ethical Data Standards: Only use data that accurately represents crime patterns, avoiding inflated statistics from over-policed neighborhoods.

In summation, Predictive AI has the potential to improve law enforcement—but at its current state, it’s misallocation of personnel and resources inflates crime perceptions, and exacerbates inequities. By restructuring data, focusing more on minimizing financial loss, and aligning patrols with real community needs, law enforcement agencies can begin to leverage AI responsibly, enhancing safety while maintaining fairness and trust.

 References

1. Amnesty International. (2025). UK use of predictive policing is racist and should be banned, says Amnesty. Retrieved from https://www.theguardian.com/uk-news/2025/feb/19/uk-use-of-predictive-policing-is-racist-and-should-be-banned-says-amnesty
   Summary: This report, titled Automated Racism, criticizes predictive policing tools for relying on biased data derived from practices like stop-and-search, disproportionately targeting Black individuals. It calls for a ban on such technologies, arguing that they modernize racial profiling and exacerbate systemic inequality. This source supports multiple points in the article about over-policing, bias, and inequitable outcomes.

2. Rodriguez, F. S. (2025). How AI is Setting the Stage for a Digital Jim Crow Era. Congressional Hispanic Caucus Institute. Retrieved from https://chci.org/wp-content/uploads/2025/03/Rodriguez_Fara_Predictive-Policing-How-AI-is-Setting-the-Stage-for-a-Digital-Jim-Crow-Era-.pdf

   F.S. Rodriguez discusses how predictive policing algorithms, by using proxies like zip codes, replicate and digitize historical racial segregation, leading to systemic biases in law enforcement practices.

3. The Markup. (2023). Predictive policing software terrible at predicting crimes. Retrieved from https://themarkup.org/prediction-bias/2023/10/02/predictive-policing-software-terrible-at-predicting-crimes

   An analysis by The Markup reveals that predictive policing software, such as Geolitica, has a success rate of less than 1% in accurately predicting crimes, raising concerns about its efficacy and reliability in law enforcement.

4. The Markup. (2023). How We Assessed the Accuracy of Predictive Policing Software. Retrieved from https://themarkup.org/show-your-work/2023/10/02/how-we-assessed-the-accuracy-of-predictive-policing-software

   This article outlines The Markup's methodology for evaluating the accuracy of predictive policing software, highlighting concerns about the tools' effectiveness and potential biases.