N※N

Why are police using facial recognition technology?

  //science-technology.news-articles.net/content/2 .. -police-using-facial-recognition-technology.html
  Published in Science and Technology on Tuesday, October 21st 2025 at 6:30 GMT by BBC
          ^{🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source}

282x179/11966 Bytes

300x168/13378 Bytes

275x183/11647 Bytes

Police Use of Facial Recognition Technology: An Overview of the Benefits, Risks, and Legal Landscape

Facial recognition has moved from the realm of science fiction to everyday reality, and law‑enforcement agencies across the United States are increasingly turning to the technology to aid investigations and public safety efforts. While the promise of quickly identifying suspects and locating missing persons is compelling, the adoption of facial recognition by police forces raises significant privacy, accuracy, and constitutional concerns. This article reviews the key points covered in the recent AOL News feature “Why Police Are Using Facial Recognition” and examines the broader context that shapes the debate.

How Facial Recognition Works in Policing

Facial recognition systems analyze a subject’s facial features—such as the distance between the eyes, the shape of the cheekbones, and the contour of the jaw—to create a unique digital “signature.” When an image or video feed is fed into the system, it compares the signature against a database of known faces. If a match is found, the software can alert officers to the identity of the individual or flag a possible suspect.

Policymakers view facial recognition as a “force multiplier” because it can process large volumes of footage in seconds, potentially reducing the time and manpower needed for manual searches. Use cases include:

• Identifying suspects in real‑time: Cameras at bus stops, airport terminals, or crowds can immediately trigger alerts if a known offender appears.
• Locating missing persons: By scanning surveillance feeds, police can pinpoint a missing child or elder in a crowded area.
• Investigative evidence: Matching images from crime scenes to databases of wanted individuals or prior arrests can help close cases.

Legal Framework and Constitutional Questions

Facial recognition in policing is governed by a patchwork of federal, state, and local laws. The primary legal concerns involve the Fourth Amendment’s protection against unreasonable searches and seizures and the requirement that law‑enforcement actions be based on probable cause or a warrant.

• Warrant requirements: In a landmark 2020 U.S. Supreme Court case (Kyllo v. United States), the Court ruled that the use of “advanced imaging technology” to obtain information not visible to the naked eye constitutes a search. While facial recognition typically uses visible camera footage, the use of proprietary matching algorithms can still be contested as a “search” of an individual’s identity.
• State bans and restrictions: California, for example, enacted Assembly Bill 1493 in 2020, effectively banning the use of facial recognition by state and local agencies unless a warrant is obtained. The law also mandates a “four‑step protocol” that requires officers to assess the accuracy of the system, conduct a “probable‑cause” analysis, and file a “data‑processing” report before deployment.
• Federal guidance: The U.S. Department of Justice’s 2021 “Guidelines for Law‑Enforcement Use of Facial Recognition Technology” recommends that agencies conduct privacy impact assessments and establish oversight committees, but it stops short of mandating a blanket ban.

Accuracy and Bias

One of the most contentious issues surrounding facial recognition is the accuracy of the systems, particularly when applied to people of color. Multiple studies—including a 2021 report by the National Institute of Standards and Technology (NIST)—found error rates of up to 9% for Black male faces, compared to 0.8% for White female faces. These disparities arise from training data sets that are heavily skewed toward lighter‑skinned, male faces, leading to higher rates of false positives and negatives for marginalized groups.

The consequences of misidentification can be severe. A wrongful alert may trigger a police chase, result in an unwarranted arrest, or create a chilling effect on community trust. In the city of Los Angeles, a 2022 incident involving a facial‑recognition misidentification of a 15‑year‑old led to a lawsuit alleging violations of civil‑rights statutes.

Public Reaction and Advocacy Efforts

Community organizations, civil‑rights groups, and privacy advocates have mounted robust campaigns against unchecked use of facial recognition. The ACLU, for example, has filed lawsuits against police departments that employ facial‑recognition without transparent oversight. In 2023, the ACLU of Washington state released a “Report on the Use of Facial Recognition by Police” highlighting the lack of independent audits and the need for public disclosure.

City councils and state legislatures have responded in varied ways. Some, like the city of Seattle, have banned the use of facial recognition by police entirely. Others, such as Miami, have instituted “black‑listing” protocols where data sets for known offenders are removed from public facial‑recognition databases to reduce bias.

Industry Players and Technological Evolution

The market for facial‑recognition technology is dominated by companies such as Amazon’s Rekognition, Microsoft’s Azure Face API, and the controversial Clearview AI. Clearview AI, in particular, has faced criticism for its data‑collection practices, as it scraped billions of images from social‑media platforms without users’ consent. In 2022, a federal court ruled that the company’s data‑collection methods violated the Computer Fraud and Abuse Act.

Some vendors have begun to offer “bias‑mitigation” tools, allowing agencies to train models on more diverse datasets or to flag high‑error cases for manual review. Nonetheless, the reliance on proprietary algorithms keeps the industry largely opaque, making independent verification of performance difficult.

The Path Forward

Balancing the benefits of facial recognition against the risks to civil liberties will require a multi‑pronged strategy:

1. Clear legal standards: Federal law should codify the circumstances under which facial recognition can be deployed, including explicit warrant requirements for surveillance in private spaces.
2. Mandatory accuracy audits: Law‑enforcement agencies must conduct regular, publicly available accuracy assessments disaggregated by race, gender, and age.
3. Transparency and community engagement: Agencies should publish data on how often facial‑recognition systems trigger alerts, the rate of false positives, and the steps taken to correct errors.
4. Technology design reforms: Vendors should provide tools for reducing bias and should allow third‑party verification of algorithmic performance.

As police forces continue to integrate facial‑recognition into their operational toolkit, the stakes for public trust and constitutional rights remain high. The debate is far from settled, and the next few years will likely see intensified legal scrutiny, technological innovation, and civic activism that will shape the future of facial‑recognition use in policing.