Why this object hangs off that object

An organisation secures the entrances to its buildings with facial recognition and uses that biometric access control to register visitors as well. When the system returns no match, a security officer reviews the camera images and decides personally whether someone may enter. The question is whose measures have to reach that officer: those of the supplier of the model, those of the department that deploys the system, or both.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A police force uses AI to search large volumes of investigation files and surface connections a detective would otherwise miss. The outputs feed into the choice of which suspect is pursued further and end up in documents that enter the criminal process. The question is whether one and the same basic instruction is enough for the analyst operating the model and for the detective who acts on its output.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A newsroom uses generative AI to prepare summaries, headlines and imagery, after which an editor finishes the piece and the desk decides to publish. Part of that work sits with freelancers, and an outside agency produces marketing content with the same tools. The question is whether your AI literacy measures must reach those freelancers and that agency, or only the people on the payroll.

Hangs off: Article 4: AI literacy

Placed against the official source | examples

A recruiter deploys an AI chat that puts candidates through a first screening conversation after they respond to a job posting, and adds their answers to their CV. The chat introduces itself with a first name and writes in a casual conversational tone. The question is whether these applicants reasonably realise that they are talking to an AI system.

Placed against the official source | examples

An organisation admits staff through facial recognition at its access control gate and additionally runs a camera in the visitor area that sorts faces into age groups. Both applications run on the same biometric infrastructure and the same images. The question is which of the two requires the people involved to be actively informed.

Placed against the official source | examples

A software company uses an AI assistant for code suggestions and code review, available only to professional developers. The same company also runs a helpdesk chatbot for customers.

Placed against the official source | examples

A bank opens an AI-driven reporting portal where customers can flag suspected fraud around their payment account or loan. The system asks follow-up questions, categorises the report and routes it to fraud detection and, where money laundering signals appear, to the internal reporting team. Because the portal concerns criminal offences, the bank assumes the disclosure duty for direct AI interaction does not apply.

Placed against the official source | examples

A municipality has an AI system write the web pages about a changed scheme for social assistance and allowances, meant to explain to citizens what they are entitled to. A communications officer reads the text for style and spelling and publishes it. The question is whether this public service thereby falls under the exception to the labelling duty.

Placed against the official source | examples

An organisation gives staff an internal AI assistant for HR, legal, procurement, compliance and IT questions. Must that assistant disclose at every turn that it is AI?

Placed against the official source | examples

A hospital deploys an interactive AI system used exclusively by properly trained health professionals to support medical diagnosis and suggest treatments. The question is whether the patient or the clinician must be told it is AI.

Placed against the official source | examples

An authority empowered by law to detect criminal offences wants to deploy an interactive AI system without telling those involved that they are communicating with AI.

Placed against the official source | examples

An employer uses an AI system that scores employee performance and lets that score weigh in promotion and dismissal. After a change to the model it turns out that a group of staff was scored too low for months, and decisions have already been taken on those scores. HR wonders whether this is a serious incident and who would have to report it.

Hangs off: Article 73: serious incident reporting

Placed against the official source | examples

An automated border gate uses biometric facial recognition to compare a traveller’s face with the photo in the passport chip. The same camera could technically also compare against a law-enforcement database, and exactly that difference decides whether this biometrics is high-risk.

Hangs off: Annex III: high-risk AI

Placed against the official source | examples

A manufacturer supplies an AI system that runs as a safety component in a machine on the production line and at the same time drives quality control. At a customer's factory an operator is seriously injured after the machine failed to stop on an anomaly. The question is who reports, to whom, and which clock is already running at that moment.

Hangs off: Article 73: serious incident reporting

Placed against the official source | examples

A municipality wants to deploy an AI system that sorts applications for social assistance benefits and indicates which files merit extra scrutiny before a case worker decides. The application is already listed in the public algorithm register. The question is what has to be in place before the first citizen passes through this system.

Hangs off: Article 27: FRIA

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A police service deploys an AI system that estimates the recidivism risk of a suspect, as an aid to the judgements later made by the prosecution service and the court. The model is subsequently retrained on newer investigative data and use is extended to a second region. The question is whether the assessment made for first use remains adequate.

Hangs off: Article 27: FRIA

Placed against the official source | examples

A university of applied sciences has an AI system rank applications for a vocational programme, using the exam results of earlier students to calibrate that ranking. A data protection impact assessment already exists for this processing. The question the school asks is whether that also covers the fundamental rights side of admission.

Hangs off: Article 27: FRIA

Placed against the official source | examples

A health insurer uses AI for risk assessment and pricing of health and life insurance. The question is whether this falls under point 5(c) of Annex III, and with that whether the Article 27 FRIA duty comes into play.

Hangs off: Article 27: FRIA

Placed against the official source | examples

A university of applied sciences uses a purchased AI system that ranks student admissions and also raises flags during digital assessment. The logs sit in the supplier environment, which hands them over on request. The teaching organisation wonders whether that settles the matter or whether the school retains a duty of its own.

Hangs off: Article 12: logging and traceability

Placed against the official source | examples

A manufacturer supplies an AI system that runs as a safety component inside the machinery of a production line and also drives quality control. The factory operating the line keeps only the alerts visible in the local controller; the rest of the recording flows to the supplier environment. The question is who has to keep which logs when it later has to be reconstructed why the line was halted.

Hangs off: Article 12: logging and traceability

Placed against the official source | examples

An employer deploys an AI system that handles task allocation among staff and summarises their performance for the performance review. HR wants to know what record of those outcomes has to be retained when an employee objects months later to a promotion decision.

Hangs off: Article 12: logging and traceability

Placed against the official source | examples

A police service fine-tunes an open general-purpose model on its own files from ongoing criminal investigations, so that detectives can see links between suspects and cases sooner. The fine-tuned model stays inside the service and is not made available to anyone else. The question is whether the service thereby becomes a provider of a general-purpose AI model itself, and so falls under Article 53.

Hangs off: Article 53: GPAI model providers

Placed against the official source | examples

A provider supplies a system that summarises employee performance data and supports HR in promotion decisions. After a year in use, departments turn out to apply it differently than intended, and managers factor the outputs into the performance review. The question is what the provider is supposed to know about that.

Hangs off: Article 72: post-market monitoring

Placed against the official source | examples

A vendor offers proctoring software that flags possible cheating during exams. Several universities of applied sciences use the system, each with its own assessment formats and its own student populations. The vendor wants to know which real-world data it must keep collecting after roll-out, and from whom.

Hangs off: Article 72: post-market monitoring

Placed against the official source | examples

A manufacturer supplies an AI safety component that halts machinery on a production line as soon as someone comes too close to the robot. The component already falls under product legislation for machinery, and the manufacturer runs quality control and incident follow-up for it. The question is what Article 72 adds on top of that.

Hangs off: Article 72: post-market monitoring

Placed against the official source | examples

A university of applied sciences uses proctoring software during an online exam and finds that a group of students is systematically and wrongly flagged as suspicious, after which grades were withdrawn. Teaching staff and the examination board want to know whether this pattern is a serious incident and, if so, who has to report it and within what time.

Hangs off: Article 73: serious incident reporting

Placed against the official source | examples

A software company supplies a SaaS platform that monitors network traffic at an operator of critical digital infrastructure and reports anomalous patterns to that customer's security team. Its developers see that use at such an operator may fall under point 2 of Annex III and wonder whether their own product therefore becomes a high-risk AI system.

Hangs off: Annex III: high-risk AI

Placed against the official source | examples

A higher education institution procures an AI system that organises student applications and enrolment and produces an admission recommendation for each candidate in its vocational programmes. The supplier says it handles the conformity assessment itself and affixes the CE marking. What is left unresolved is whether the institution still has a step of its own to take before the system goes into use in its teaching.

Hangs off: Articles 43-49: conformity assessment, CE and registration

Placed against the official source | examples

A hospital uses AI to prioritise incoming patients at the emergency department. The question is not whether the system is high-risk but by which route: as a medical device under product legislation, or directly under Annex III.

Hangs off: Annex III: high-risk AI

Placed against the official source | examples

A non-food retail chain lets customers pay later in its webshop and decides at checkout whether a consumer qualifies. The team builds that assessment system in house, on top of a pre-trained model supplied by a vendor. The question is what has to be on record before the feature goes live, and who has to put it there.

Hangs off: Article 11: technical documentation

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