How Europe’s ‘Third Way’ on AI Justifies Turning Citizens Into Data Sacrifices

A single city square in Hamburg has become the testing ground for one of the most consequential questions in European technology policy: whether the continent’s celebrated middle path between surveillance capitalism and the surveillance state can actually deliver on its promises. A new study published in the journal AI & Society by Philipp Knopp of Chemnitz University of Technology dissects the parliamentary debate surrounding an experimental AI-powered CCTV system deployed by the Hamburg state police, and reaches a conclusion that should unsettle anyone who believes regulation alone can tame artificial intelligence. The research introduces a striking new concept, the data sacrifice, to describe what gets given up, often invisibly, when societies decide that training an algorithm is worth the cost.

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