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 research, published on 19 February 2026 as volume 3, article 11 of the journal, rests on a structured literature review and conceptual analysis rather than new empirical data. Pesek draws on governance theory and on critical scholarship concerning digitalisation, platformisation, and datafication to build an analytical framework capable of describing what actually happens when AI enters schools. The core insight is that AI behaves as a systemic and transversal actor. Unlike a textbook or a learning management system, it does not sit neatly inside a single layer of the education system. It operates across the boundary between centralised regulation and decentralised educational practice, shaping decisions at the ministry level and in individual classrooms simultaneously, often without either policymakers or teachers fully noticing.

To make this complexity tractable, the paper introduces a conceptual distinction between three layers of educational AI. The first layer consists of foundational AI infrastructures, essentially the large AI models on which everything else depends. The second layer is AI content, the generated material that flows into lessons, assessments, and learning resources. The third layer comprises AI tutors, the interactive systems that increasingly mediate the relationship between learners and knowledge. Pesek argues that these three layers demand different governance responses, because they concentrate power, risk, and accountability in different places. Treating them as a single undifferentiated category of “AI tools,” he suggests, is one of the reasons existing policy frameworks have struggled to keep pace.

The analytical heart of the paper is a reconfigured hybrid governance model. Hybrid governance refers to arrangements in which authority is shared between state regulators, commercial platform providers, local institutions, and professional educators, rather than residing in any single actor. The study shows that AI has reconfigured these hybrids in ways that were not anticipated by earlier digital education policy. When a school adopts an AI tutoring system, for example, decisions about what counts as mastery, how feedback is phrased, and which learning pathways are offered are no longer made solely by curriculum authorities or teachers. They are partly encoded in models trained on data that reflect commercial priorities and statistical patterns far removed from any school’s local context. Governance, in other words, has been partially delegated to infrastructure.

From this analysis, Pesek identifies four key governance risks. The first is the homogenisation of learning processes, in which widely deployed AI systems push diverse classrooms toward similar rhythms, formats, and definitions of success, narrowing the space for pedagogical diversity. The second is intensified surveillance, as AI-mediated platforms continuously capture data on how students read, write, hesitate, and solve problems, extending the datafication of education into its most intimate moments. The third risk is blurred accountability: when an AI system produces a misleading explanation, an unfair assessment, or a harmful recommendation, it is often unclear whether responsibility lies with the developer, the platform, the school, or the teacher. The fourth risk is the erosion of student and teacher agency, as both groups find their decisions increasingly shaped by opaque recommendation engines rather than by professional judgement or learner choice.

These risks are not hypothetical. The paper situates them within a well-documented policy trajectory. International organisations such as the OECD and UNESCO have repeatedly highlighted both the promise and the governance gap of educational technology, from the OECD’s Digital Education Outlook 2023 to UNESCO’s 2021 guidance for policy-makers on AI and education. Critical scholars including Neil Selwyn and Ben Williamson have long warned that the digitalisation of schooling concentrates power in platform companies and reconfigures educational purposes around data and measurement. Pesek’s contribution is to connect these strands into a single governance argument: the problem is not merely that AI may be used badly, but that the structures through which education systems steer themselves are no longer adequate to the technology they now contain.

The proposed response is a reconfigured hybrid governance approach that differentiates responsibilities across system levels and across AI functions. At the level of foundational AI infrastructures, the paper points toward regulatory oversight of the models themselves, including transparency about how they are trained and evaluated. At the level of AI content, accountability mechanisms need to establish who is answerable when generated material is inaccurate, biased, or inappropriate for a given age group. At the level of AI tutors, governance must protect the integrity of the learner-teacher relationship and ensure that interactive systems support, rather than supplant, pedagogical relationships. Crucially, the framework insists that these responsibilities cannot all be discharged centrally; they must be distributed across national regulators, intermediate authorities, school leaders, and classroom professionals in a deliberate and explicit way.

The paper also advances concrete policy recommendations aimed at operationalising this approach. These include regulatory oversight tailored to the three-layer distinction between AI models, AI content, and AI tutors; accountability mechanisms that make responsibility traceable across the chain from developer to classroom; and explicit protection of educational purpose and professional autonomy, so that teachers retain the authority to interpret, adapt, and when necessary refuse AI-mediated recommendations. The underlying principle is that governance should safeguard democratic values and human-centred education while still allowing education systems to harness the genuine benefits of AI, such as personalised support and reduced administrative burden. The alternative, the paper implies, is a drift in which governance defaults to the incentives of platform providers rather than the purposes of public education.

What makes the study distinctive is its refusal to treat AI in education as a purely technical or ethical dilemma to be solved at the level of individual classrooms. By foregrounding governance as the central analytical and policy concern, it reframes the debate: the question is not only whether an AI tool helps students learn, but who holds power over the systems reshaping learning, who answers when those systems fail, and how the distribution of authority can be redesigned deliberately rather than left to market forces. For policy-makers, school leaders, and educators confronting the accelerating arrival of AI in education, the message is that the deepest challenges are structural. The technology has already reconfigured the architecture of educational governance; the task now is to reconfigure it back, with accountability, transparency, and human agency as explicit design goals rather than afterthoughts.

Subject of Research: Governance of artificial intelligence in education systems

Article Title: Rethinking Education Governance in the Age of AI

Article References: Pesek, I. (2026). Rethinking Education Governance in the Age of AI. Frontiers of Digital Education, 3(1), Article 11. https://doi.org/10.1007/s44366-026-0085-z

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0085-z

Keywords: AI in education, education governance, hybrid governance, accountability, datafication, platformisation, AI tutors, surveillance, teacher agency, education policy, EdTech, governance risks