Track B
„How can AI detect dangerous situations early without violating the fundamental rights of uninvolved people?"
A safety architecture that detects collective anomalies rather than classifying persons. Civil liberties by design, not as an add-on.
Motivation
There is no off-the-shelf technical solution in the space between security and fundamental rights. Classical predictive-policing systems operate with person-level risk scores; Art. 5(1)(d) of the EU AI Act prohibits exactly that. Art. 5(1)(h) likewise restricts biometric remote identification in public spaces, with narrow exceptions.
The question, therefore, is not how to circumvent these prohibitions, but what an architecture has to look like to be useful under them. This research answers with a methodology that models collective dynamics instead of individuals — and that makes protecting helpers (rather than classifying suspects) the explicit objective.
Methodological approach
Instead of deterministic person classification, the three-outlier model produces exclusively calibrated probabilistic indications of statistical anomalies in collective dynamics. No person-level labels, no biometric identification, no suspicion generation — only an indication system for human decision-makers.
The second load-bearing pillar is helper protection as the objective: the system is optimised to detect and protect helpful behaviour rather than to produce suspect cases. This reversal of the objective changes the whole architecture — from annotation to threshold calibration.
The model, visualised
The Three-Outlier model distinguishes roles in a crisis situation rather than classifying individuals. Helping people are structurally protected; fundamental-rights guardrails are written into the architecture.
Core components
Distinguishes three roles in a crisis situation: source (initiating), helper (intervening, assisting) and knower (contextually reacting bystander). The role differentiation structurally protects helping individuals from being misclassified as initiators.
Optimisation not for suspicion but for the detection and protection of helpful behaviour. Inverts the detection logic.
Annotation schemes are designed to avoid facial, gait, and voice features. Data minimisation as an architectural decision.
Eight methodological corrections defined in the research-ethics document that every implementation of this architecture must satisfy. They constitute the civil-liberties architecture.
Probability statements are calibrated against publicly available recordings of documented incidents. No training on personal data without a research clause.
Notified-Body pre-assessments (TÜV SÜD, TÜV Rheinland, Bureau Veritas) and legal opinions on the fundamental-rights conformity of the architecture are foreseen.
Legal frame
Get involved
Universities with a public-safety focus, public authorities at the BMI/BSI/BBK level, and research consortia along BMBF SIFO and Horizon Europe Cluster 3 are explicitly invited to get in touch.
public-sector@zensation.aiMore from this research
Three tracks, one core, a shared ethics foundation.