The European approach to AI is frequently marketed as a third way, an alternative to both an AI-powered surveillance capitalism and an AI-powered surveillance state. Landmark instruments such as the AI Act, the General Data Protection Regulation and the Digital Services Act are held up as proof that Europe can foster trustworthy AI while safeguarding democratic values. The word while does heavy lifting here, signaling a compromise between value systems that are supposedly reconciled rather than traded off. Knopp argues that this compromise is not merely a policy stance but a mode of justification, one that selectively absorbs some criticisms while excluding others in order to establish the moral conditions under which data extraction becomes possible at all.

The case at the heart of the study is the Hamburg police’s trial of automated behavioral recognition on Hansaplatz, a square in a predominantly migrant neighborhood undergoing gentrification. The system relies on algorithmic pose estimation: digital video frames are converted into vector graphs, producing what proponents call digital skeletonization, a normalized, decontextualized and depersonalized representation of human bodies. Unlike facial recognition, the software strips away body size, skin color, hair, clothing and body shape, then classifies movements against a predefined set of police-relevant activities such as beating, kicking or defensive posture. A three-month proof-of-concept ran in 2023, and by February 2025 the city parliament had passed the first German law explicitly permitting police to transfer both anonymized and non-anonymized surveillance data to external partners for machine learning.

Getting to that point required enormous sociotechnical investment, what the study, drawing on the pragmatist sociology of Laurent Thévenot, calls form investments. The square itself had to be engineered into something like a laboratory, with AI-ready cameras, an interoperable CCTV system, high-security data infrastructure and an unobstructed sightline across the surveilled space. CCTV operators had to learn to label the system’s outputs, maintain performance logs and interpret the different ways it produces false alarms. Because public spaces rarely yield sufficient footage of atypical behavior, police in Mannheim, where the system was first implemented, even staged show fights in front of cameras, with riot police enacting scripted violence to generate training data. These theatrical performances, the study notes, risk inscribing selective police assumptions about what violence looks like directly into datasets and classification algorithms.

Knopp’s central analytical move is to name these costs data sacrifices. Datafication, he argues, is never a neutral capture of reality but a costly operation of appropriation, exclusion and reductive generalization, one that abstracts traces of events from the complex situations in which they occur. The concept does double duty: it highlights the material and social expenses of formatting the world into data, and it foregrounds the critical agency of social actors who point out what has been disregarded. Where the influential notion of the boundary object emphasized dynamic compromises between local knowledges, the data sacrifice directs attention to the moral work that presents these efforts and exclusions as worthwhile contributions to a higher common good.

Using Critical Discourse Analysis of 57 parliamentary documents spanning from the conservative opposition’s first request in May 2019 to the passage of the new law in February 2025, the study reconstructs five suborders of worth through which the system was justified and attacked: security, individual freedom, social justice, automation and experimentalism. The conservative CDU opened the debate with a catastrophic picture of declining safety, describing citizens who hardly dare to walk alone in the dark and squares that had become crime hotspots plagued by fights and knife attacks. Such narratives perform a dual function, establishing the legal grounds for CCTV while constructing moral urgency around the high discursive value of security.

What made the AI project politically viable, however, was not the old zero-sum logic in which freedom is traded for security. Instead, proponents framed automation itself as the compromise. Conventional CCTV was criticized as inefficient, with one Social Democrat noting that officers have to watch twenty screens all day long, while AI promised to automate attentiveness, cut costs and enable real-time preemption. More remarkably, the technology was sold as a civil-liberties upgrade: because pose estimation supposedly erases personal traits, the innocent bystander remains out of sight, is not stored in any records, and, in the words of one conservative parliamentarian, has nothing to fear. This framing allowed the Green Party, historically rooted in surveillance-skeptic social movements, to reposition itself as a supportive but watchful partner, declaring itself open to the outcomes of the trial while endorsing its potential.

The same logic was extended to social justice. Because the stick figures produced by skeletonization carry no age, gender or ethnicity, proponents claimed the system could not discriminate, positioning it as superior both to human operators and to facial recognition, which had once made the identical promise. Critics from the Left Party saw a discriminatory trap instead, arguing that people with physical disabilities whose movements differ from those classified as normal, or homeless people who sleep rough, would be flagged as atypical, and that discrimination begins the moment someone is captured and unjustly observed, not when police intervene. Rather than finding common ground, these critics were excluded from legitimate debate, dismissed as ideological, ignorant or dishonest by parties that otherwise shared the same civic vocabulary.

When present-day failures could not be justified by the system’s actual capabilities, proponents turned to experimentalism, the solutionist order of worth that frames innovation as an end in itself and an external compulsion. Hamburg was portrayed as lagging behind other federal states; AI was declared to be everywhere, making it quite unrealistic not to exploit it; and German-developed software was celebrated as a matter of competing with American and Chinese surveillance markets. Trial and error became the sole pathway to progress, justifying security risks, costly infrastructure and intrusions into civil rights as temporary sacrifices on the road to an imagined future in which all contradictions would finally be resolved. The study argues that this radical temporalizing of moral paradoxes turns the object of criticism into a moving target and undermines public accountability, since evaluations of present failure are always deflected toward a not-yet-existing entity designed to withstand critique.

The paradoxes culminated in the new Hamburg law, which authorizes the transfer of citizen surveillance data to external developers for machine learning and has since been adopted in other German states. Knopp’s conclusion is pointed: the third way does not simply balance innovation and rights, it equips social control and data extraction with moral underpinnings, internalizing select civic values and critics while excluding others, until the industrialization of surveillance itself becomes a civic value. As local AI trials proliferate across Europe, the study suggests that the decisive battles over what algorithms may see, what data they may consume and who pays the price will be fought not in Brussels policy papers but in precisely such local disputes, where the sacrifices are made concrete and the justifications, for those willing to look, become visible.

Subject of Research: Parliamentary justification and critique of data extraction in an AI-based CCTV surveillance trial in Hamburg, Germany

Article Title: Data sacrifices and the ‘third way’ toward AI: justification and critique in local conflicts over automated surveillance

Article References: Knopp, P. (2026). Data sacrifices and the ‘third way’ toward AI: justification and critique in local conflicts over automated surveillance. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03075-w

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03075-w

Keywords: artificial intelligence, surveillance, datafication, CCTV, police, Hamburg, third way, critical data studies, pose estimation, machine learning, parliamentary discourse, civil liberties

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

The study does not examine Flock cameras directly. Instead, it draws its evidence from two other contested urban technologies: facial recognition systems in Portland, Oregon, and autonomous vehicles in Tempe, Arizona. By tracing how those communities confronted the promised benefits of new tools against risks to privacy, equity, transparency and civil liberties, the researchers built a framework that applies directly to the current Flock controversy. The choice of cases was deliberate. Both technologies arrived in cities amid claims of improved safety and efficiency, and both raised documented concerns about bias, accuracy and the control of sensitive data. The paper, co-authored by Farah Najar Arevalo of Arizona State University and Devon McAslan of Chalmers University of Technology in Sweden, was published on 18 August 2026 under the title Public value mapping in the smart city: Assessing emerging urban technologies.

Timing matters in this research. Miller notes that the technologies investigated in the study were emerging slowly when the work began, before and during the COVID-19 pandemic. Since then, the pace of adoption has accelerated dramatically. These technologies, like facial recognition and Flock, were emerging in baby steps then, and the pace has only ramped up, Miller observes. That acceleration places municipal governments in a difficult position. Cities and towns face pressure from technology companies and law enforcement agencies to adopt systems that promise gains in public safety or operational efficiency, often without the staff or technical expertise needed to understand the full range of trade-offs involved.

That capacity gap is central to the problem the study identifies. Many local governments do not have the capacity or sometimes the expertise to think through how emerging technologies might affect a whole set of values that the community may have, Miller explains. A city might adopt a surveillance system to fight crime, for example, without fully considering its implications for privacy, freedom of expression, racial equity, or how the information it gathers could be shared beyond the agency that collected it. In practice, procurement decisions are frequently made department by department, with police or public works offices evaluating a tool narrowly on its stated function while the broader civic consequences go unexamined until a controversy erupts.

The Portland case illustrates those tensions in concrete detail. Researchers found that facial recognition cameras could offer public-safety benefits, but there was significant uncertainty about the extent of those benefits in an urban environment. At the same time, there were documented concerns about racial and gender bias in the technology, questions about its accuracy, and a lack of transparency about how data would be collected, managed and shared. The uncertainty cut both ways: the promised gains could not be verified, while the potential harms were grounded in documented evidence about how facial recognition performs across demographic groups. That asymmetry proved decisive in how the community weighed its options.

Ultimately, Portland’s response was to ban facial recognition technology in public spaces. The outcome carries a broader lesson for cities confronting Flock cameras and similar systems: technologies marketed as inevitable can be constrained when the community evaluates them through the lens of public values. The ban did not emerge from a single moment of opposition but from a process in which residents and officials examined what the technology was designed to do and what else it might change. That distinction, between a tool’s intended purpose and its full range of effects, is the analytical core of public value mapping and the reason the researchers believe it generalizes across technologies and jurisdictions.

Miller is careful to caution that public value mapping is not a scorecard that automatically tells a community whether to approve or reject a technology. Instead, it is intended to bring government officials and residents together to identify what they value, examine potential benefits and harms, and consider what policies could produce a better outcome. The method treats values such as privacy, equity, transparency and civil liberties not as obstacles to innovation but as legitimate design criteria that a technology deployment either respects or violates. In that sense, it resembles a structured civic deliberation more than a technical audit, and it deliberately shifts the conversation from the artifact itself to the governance choices surrounding it.

Applied to the Flock debate, the framework suggests asking a series of questions before debating the pros and cons of the cameras themselves. What public safety problems actually exist in the community? What privacy sacrifices are people willing to accept? Who controls the data the cameras generate? How long is it retained? Who can access it? And, crucially, do the answers align with the community’s stated values? These questions expose the fault lines that often remain hidden in polarized debates, where opponents and supporters talk past each other about the device rather than about the data governance regime built around it. The study asserts that those conversations should take place across government silos and with the public before a controversy erupts, not after.

The stakes of getting this process right are considerable. License plate reader networks create searchable databases of vehicle movements, and controversies over data-sharing agreements between municipalities, private companies and out-of-state law enforcement agencies have made Flock cameras a national flashpoint over privacy, surveillance and policing. A framework that forces explicit answers to questions of retention, access and control could change how such contracts are negotiated, disclosed and audited. It could also reshape the market itself, since vendors respond to the procurement standards that cities set. If communities demand transparency about data flows as a condition of adoption, the terms of deployment shift from a take-it-or-leave-it pitch to a negotiated public arrangement.

Miller says the goal is not to reject innovation but to approach it deliberately, ensuring that technological advancements do not come at the expense of values a community is trying to protect. Technology, he argues, is not destiny: communities can have a say in how it is adopted, regulated and used. We shape technologies every day in all sorts of ways, Miller adds. The question is, can we do so with more intention? For hundreds of cities now weighing Flock cameras, facial recognition and the next wave of smart-city tools, that question may define whether emerging surveillance infrastructure is governed by default settings written elsewhere or by the deliberate choices of the people it watches.

Subject of Research: Public value mapping for assessing emerging urban surveillance and smart city technologies

Article Title: Flock cameras put cities at a crossroads. New research offers a roadmap

Article References: Flock cameras put cities at a crossroads. New research offers a roadmap. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Flock cameras, public value mapping, surveillance, facial recognition, automated license plate readers, urban technology governance, privacy, civil liberties, smart cities, Portland, Tempe, technology policy