How Five Democracies Are Clashing Over Facial Recognition in Policing

Facial recognition technology has quietly become one of the most consequential tools in modern law enforcement, capable of matching a face captured on a surveillance camera against vast databases of known individuals within seconds. Yet according to a new comparative study published in Global Public Policy and Governance, the rules governing how democratic governments deploy this technology for arrests are inconsistent, fragmented, and in many places dangerously unclear. Researchers led by Pedro Robles of Penn State Lehigh Valley, together with Daniel J. Mallinson, Eric Best, Cheryl Devaney, and Lauren Azevedo, examined how five developed democracies—the United States, Canada, Germany, Italy, and France—regulate the use of facial recognition technology in criminal justice, and their findings reveal a striking patchwork of approaches that leaves privacy, accountability, and civil liberties exposed to very different degrees of risk depending on where a citizen happens to live.

The technology itself has traveled an extraordinary distance since its origins in the 1960s, when Woodrow Wilson Bledsoe’s early systems required researchers to manually plot the coordinates of facial features on photographs. Decades of advances in pattern recognition and computing transformed those laborious beginnings into today’s artificial intelligence-powered systems, which can automatically detect, encode, and match faces across massive image collections in real time. For law enforcement, the appeal is obvious: retrospective identification of suspects, generation of investigative leads, location of missing persons, verification of arrested individuals who refuse to identify themselves, and rapid screening of crowds at airports, stadiums, and border crossings. Under ideal conditions, top-performing systems achieve impressive accuracy. But real-world deployments rarely offer ideal conditions, and the study emphasizes that poor lighting, obscured views, and low-quality imagery can significantly degrade performance precisely in the situations where police are most likely to rely on the technology.

The technical weaknesses are not evenly distributed across the population, and this is where the ethical stakes become acute. Testing conducted by the U.S. National Institute for Standards and Technology in late 2019, covering nearly 200 facial recognition algorithms, found that Asians, African Americans, and American Indians were more likely to be misidentified than white individuals, that women were misidentified more often than men, and that children and the elderly were misidentified more often than middle-aged adults. Researchers attribute these discrepancies largely to unrepresentative training data, and some newer algorithms show no detectable bias, suggesting the problem is fixable through better development practices. In the meantime, however, the consequences are real: the study notes six documented wrongful arrests of Black individuals in the United States connected to facial recognition use, a figure that likely understates the problem because no comprehensive data exists on how extensively American law enforcement actually deploys these systems.

The United States exemplifies regulatory fragmentation. There is no federal law governing facial recognition, leaving states and cities to experiment on their own. San Francisco became the first American city to ban the technology for public agencies in 2019, followed by Oakland, California, and Somerville, Massachusetts, and more than twenty-two local governments have adopted surveillance oversight rules modeled on an American Civil Liberties Union template. At the same time, private actors have operated at extraordinary scale with little constraint. Clearview AI scraped tens of billions of facial images from the public internet, including from social media platforms, without the knowledge or consent of the people pictured, prompting the ACLU to sue under Illinois’ Biometric Information Privacy Act. The company was barred from selling its system to most private businesses, yet it continues to sell access to state, local, and federal law enforcement agencies across the country.

Canada’s experience illustrates a different failure mode: adoption outpacing oversight. The Royal Canadian Mounted Police used Clearview AI until public outcry halted the practice in 2020, and reporting revealed the force had also employed other tools, including Amazon’s Rekognition for combating human trafficking of minors and other platforms for counterterrorism work that were later abandoned over privacy concerns. In February 2021, the Office of the Privacy Commissioner of Canada determined that Clearview AI’s app was illegal under Canadian privacy law, which requires consent for the use of personal data. By 2022, the Commission issued guidance for police use of facial recognition while candidly acknowledging that no specific legal framework exists in Canada, only a patchwork of federal and provincial privacy statutes, police powers legislation, and Charter jurisprudence. Unlike the United States, however, Canada benefits from a national privacy commission alongside provincial and territorial commissioners, such as the Ontario Information and Privacy Commissioner, which has issued detailed recommendations emphasizing accountability and transparency for police use of the technology.

Germany stands out as the most restrictive of the five cases, a stance rooted in both history and law. The European Union’s General Data Protection Regulation, particularly Article 9, prohibits the processing of biometric data unless justified by public interest, law enforcement needs, or explicit consent, and Germany’s Federal Data Protection Act reinforces these protections for both private businesses and public agencies. Concepts such as the right to be forgotten make deploying facial recognition in public spaces exceptionally difficult, effectively confining the technology to law enforcement applications subject to judicial oversight. German politicians have pushed for bans on facial recognition in public spaces and have resisted provisions of the EU’s Artificial Intelligence Act that would permit warrantless use by police. A telling test came in 2017, when authorities ran a prototype project at Berlin Südkreuz railway station attempting to identify consenting volunteers at known times; the accuracy results were described as underwhelming. Despite continued advocacy by public agencies to expand facial recognition for public safety, surveys consistently show German citizens are less supportive of the technology than their peers in other nations.

Italy and France, both unitary states with uniform national policies, have taken sharply divergent paths. Italy deployed thermal facial recognition during the COVID-19 pandemic to monitor travelers at airports, schools, and train stations, but in the pandemic’s wake the country prohibited biometric facial recognition except for judicial investigations and crime fighting. After the Clearview AI data leak scandal, Italy’s data protection authority, the Garante, banned use of the technology on limited legal grounds, and a moratorium on facial recognition video surveillance in public spaces remains in place through 2025, with fines imposed for violations. France, by contrast, has embraced the technology, most visibly through the mass surveillance apparatus deployed for the 2024 Paris Olympic Games under public safety exceptions. Critics, including civil society organizations, argue that French deployments often occur without meaningful explicit consent and that the government has prioritized security and efficiency over individual privacy, raising fears of a drift toward a surveillance state within an EU framework nominally designed to prevent exactly that.

The comparative analysis, organized around three dimensions—legal compliance, ethical alignment, and effectiveness of implementation—shows that political structure shapes regulatory outcomes. Federal systems like the United States and Canada produce fragmented rules that vary by state or province, which enables policy experimentation but creates inconsistencies that, combined with the technology’s demographic accuracy gaps, can produce unequal policing outcomes across communities. Germany’s federalism is less fragmented because national policy sets a restrictive floor. Unitary systems can impose uniform rules quickly, but that uniformity cuts both ways: France could rapidly extend facial recognition nationwide for the Olympics, while Italy could just as rapidly impose a nationwide moratorium. On consent, the five countries are largely similar in one troubling respect: explicit consent is generally not required for facial recognition use in the criminal justice system, even though it is typically required in other settings, with Canada allowing warrantless-style use only where an explicit need exists.

The authors argue that no existing AI ethics framework specifically addresses facial recognition in criminal justice, and they call for one built on privacy, accountability, and consent, incorporating mechanisms such as the GDPR’s privacy by design and privacy by default principles and mandatory Data Protection Impact Assessments for high-risk applications. Embedding these safeguards at the design stage, they contend, would surface technical risks before systems reach the street. The study also warns that diminished public trust in governance breeds skepticism even toward well-intentioned deployments, and that the chaotic rollout of facial recognition in many countries has deepened that distrust. Global institutions such as the United Nations will need to grapple with ethical principles for facial recognition and artificial intelligence more broadly, while domestic policymakers balance international guidance against the demands of their own citizens—no small task given how much public attitudes toward privacy and security vary across nations.

The research has limits the authors acknowledge freely: it relies on secondary sources, covers only five developed democracies, and excludes authoritarian regimes where facial recognition poses the gravest human rights risks, such as the European Court of Human Rights ruling against Russia for using the technology to arrest political protestors. Future work, they write, should gather primary data from policymakers, legal experts, and the public, extend the comparison to less developed democracies and autocracies, and flesh out an ethical framework adaptable to different political, social, and cultural contexts. What the current analysis makes unmistakably clear, however, is that the technology is already embedded in democratic policing, its error rates fall hardest on marginalized groups, and the absence of clear, tailored legislation has left citizens’ rights protected unevenly at best. As cameras multiply and algorithms improve, the window for democracies to regulate deliberately rather than reactively is narrowing—and the five countries studied here offer both a warning and a menu of possible paths forward.

Subject of Research: Comparative regulation of facial recognition technology in criminal justice arrests across five democracies

Article Title: Global perspectives on regulating facial recognition technology utilization for criminal justice arrests

Article References: Robles, P., Mallinson, D. J., Best, E., Devaney, C., & Azevedo, L. (2025). Global perspectives on regulating facial recognition technology utilization for criminal justice arrests. Global Public Policy and Governance, 5(2), 186-204. https://doi.org/10.1007/s43508-025-00117-9

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00117-9

Keywords: facial recognition, criminal justice, privacy, GDPR, algorithmic bias, law enforcement, surveillance, AI ethics, Clearview AI, data protection, civil liberties, comparative policy