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required.","topics":["evidence","value-chain"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:value-chain-representative"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[],"legal_status":null,"deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"editorially_reviewed","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-4-gezichtsherkenning-toegangscontrole","legacy_id":"raip:example:example-artikel-4-gezichtsherkenning-toegangscontrole","type":"example","slug":"example-artikel-4-gezichtsherkenning-toegangscontrole","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"595c5cc236618a40fa4882c79603f0febc9762b4165cdf0ac60b70bdbbe5c457","label":"Facial recognition at access control: the guard behind the camera counts too","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-4-ai-literacy"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 4(1) provides that providers and deployers of AI systems shall take measures to support the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf. In doing so they must take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, and consider the persons or groups of persons on whom the AI systems are to be used. The same provision states that this obligation does not require providers or deployers to guarantee any specific level of AI literacy of any individual.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read the phrase about the persons on whom the system is used as the centre of gravity for biometrics: whoever stands in front of the camera is subject to the outcome and has little to set against it. That argues for equipping the officer who decides for himself when no match comes back more substantively than the colleague who merely switches the system on and off. The article itself names no sufficient level and expressly states that you need not guarantee one, so where the floor lies for each role stays open. In our assessment a record kept per role, stating the choice made and the reason for it, is easier to defend than one organisation-wide session backed only by an attendance list.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-4-politie-opsporingsanalyse","legacy_id":"raip:example:example-artikel-4-politie-opsporingsanalyse","type":"example","slug":"example-artikel-4-politie-opsporingsanalyse","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"60ac6751cd1959be17d92f129f223cca3fae064e4dfa4eed6759a8d2f88b5297","label":"Police using AI in investigations: context sets how deep the training goes","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-law-body"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-4-ai-literacy"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 4(1) requires providers and deployers of AI systems to take measures supporting the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf. The provision prescribes that they take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, as well as the persons or groups of persons on whom the AI systems are to be used. It also states that this obligation does not require any specific level of AI literacy of any individual to be guaranteed.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Article 4 requires you to weigh the context of use and the people the system is applied to, and in law enforcement both factors run high on our reading. Whether a general introduction to what AI can do is then enough for someone carrying an output into a file that affects a person's position as a suspect, we doubt, but the provision expressly names no level you must guarantee, so that floor is yours to justify. We would record for each role what someone must be able to recognise, for instance that a discovered connection is not yet evidence, and revisit that choice periodically.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-4-redactie-generatieve-content","legacy_id":"raip:example:example-artikel-4-redactie-generatieve-content","type":"example","slug":"example-artikel-4-redactie-generatieve-content","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"c17ece0eea8b5b5b7f25d80eb389c31dbc5e18c57449d3e19d41d7ba2b0a8658","label":"Newsroom with generative AI: do freelancers count within your measures?","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-4-ai-literacy"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 4(1) is addressed to providers and deployers of AI systems and requires them to take measures supporting the development of AI literacy of their staff and of other persons dealing with the operation and use of AI systems on their behalf. The provision requires them to take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, as well as the persons or groups of persons on whom the AI systems are to be used. It does not require any specific level of AI literacy of any individual to be guaranteed.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Alongside staff, the text expressly names other persons dealing with the operation and use of AI systems on your behalf, and we read that as a functional boundary rather than a contractual one. On that reading a freelance editor using your tool inside your workflow and on your instruction sits within your measures, employment contract or not. The outside agency is a harder case: if it works in your environment and on your instruction, the argument that it acts on your behalf holds up, but if it runs its own tools on its own account it is a deployer in its own right, and Article 4 does not say your measures must cover that work. In practice, in our assessment, that means recording in your agreements who works in which role and what instruction you give, rather than trusting the other side to arrange it.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 4(1)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-ai-chat-sollicitanten","legacy_id":"raip:example:example-artikel-50-ai-chat-sollicitanten","type":"example","slug":"example-artikel-50-ai-chat-sollicitanten","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"bbf1f3c5c8847982509ad7a4d086b513a5e0742db0e4415b178f1cdd5cfe1bc2","label":"AI chat in recruitment and selection: what the applicant must be told","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 50(1) requires providers to ensure that AI systems intended to interact directly with natural persons are designed and developed in such a way that the natural persons concerned are informed that they are interacting with an AI system, unless this is obvious from the point of view of a natural person who is reasonably well-informed, observant and circumspect, taking into account the circumstances and the context of use. Article 50(5) provides that this information must be given to the natural persons concerned in a clear and distinguishable manner at the latest at the time of the first interaction, and must conform to the applicable accessibility requirements.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 50(1) and (5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read the obviousness test as a question about the audience the system actually meets, and in recruitment and selection that audience is not a trained professional group but a broad set of applicants under pressure who do not yet know the organisation. A human first name and a casual tone push that assessment the wrong way in our view, however well they convert. If you buy the chat rather than build it, Article 50(1) is by its wording addressed to the provider, while you are the one choosing the persona and the entry point; whether putting your own name on such a chat moves you into the provider role is a question Article 50 does not answer. So make the disclosure in the first message a procurement requirement and verify at delivery that it is genuinely there.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 50(1) and (5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-camera-toegangscontrole-categorisatie","legacy_id":"raip:example:example-artikel-50-camera-toegangscontrole-categorisatie","type":"example","slug":"example-artikel-50-camera-toegangscontrole-categorisatie","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"160b7164fde8818b8b2563f5a2e755e5669228072717a49f653bf2ca4c75bee6","label":"Camera at the entrance: access control versus biometric categorisation","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 count automated facial-recognition access controls among the systems that merely collect data passively and are not capable of an exchange with natural persons, and therefore do not interact within the meaning of Article 50(1). For Article 50(3) they state that, unless the use is prohibited under Article 5(1)(g), the information duty applies to any biometric categorisation system, including outside the high-risk scope, and they give classification by age or gender on the basis of biometric data as an example. Article 50(3) itself carries a further exception for systems permitted by law to detect, prevent or investigate criminal offences. As a way of informing people the guidelines describe a visible notice at each possible entrance to an exhibition room stating that facial images are captured to assign visitors to an age group, provided at the latest at the moment of first exposure.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, point (30) and points (104) to (108)","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Our reading is that you should map this per processing purpose rather than per camera: the same lens that stays outside Article 50(1) at the access control gate moves inside Article 50(3) as soon as those images place people in a category. Record for each setup what happens to the capture and who the deployer is, because that decides whether a notice belongs at the entrance. The guidelines prescribe no fixed form, but they do fix the moment: the notice has to be there before someone walks into frame, and a line in the privacy statement rarely makes that moment, in our reading.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, point (30) and points (104) to (108)","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-code-assistent","legacy_id":"raip:example:example-artikel-50-code-assistent","type":"example","slug":"example-artikel-50-code-assistent","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"fe318cc5831d962473121e1af68d1c399dd3a01e1b3bd89e04d554385ee6a460","label":"Code assistant for developers: an exception, until it faces outward","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 list code assistance and code review chatbots available only to professional developers as an example where the obviousness exception applies. Chatbots embedded in online platforms or helpdesks, where users may perceive outputs as human-generated, they list as an example where the disclosure duty does apply.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Two AI assistants at the same company, two outcomes. Assess per application who the user is and what they reasonably expect, rather than taking one organisation-wide position on chatbots.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-fraudemeldportaal-bank","legacy_id":"raip:example:example-artikel-50-fraudemeldportaal-bank","type":"example","slug":"example-artikel-50-fraudemeldportaal-bank","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"582aea2e3144049ca4f842c782162a26e0e60300ad221f75347d55f99d05a125","label":"Fraud reporting portal at a bank: why the law enforcement exception drops out","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 expressly list AI-assisted fraud reporting hotlines and digital portals operated by financial institutions or public authorities, where users can report suspected financial crimes, as an example that is not exempted and falls within the scope of Article 50(1). They explain that the exception does not apply where the system is available to the public and offers individuals the functionality to report a criminal offence. The fact that the system only gathers details, categorises and prioritises the report while human investigators validate the information before any action is taken does not change that in the guidelines.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, points (48) and (49), list of examples under the law enforcement exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"In our reading this is where banks most often take the wrong turn: the reporting portal sits organisationally under fraud and compliance, which makes the law enforcement exception feel natural, while it is precisely the public reporting channel that is carved out of that exception. On top of that, the exception is written for use authorised by law for law enforcement purposes, and the guidelines stretch it no further than to other public authorities holding such a legal basis, so in our reading a private bank rarely stands in it at all, before the public reporting channel even comes up. Treat the portal as an ordinary public-facing chat and put the disclosure in the first screen of the conversation. A line in the terms and conditions or a product leaflet is a weak choice for this channel, because by then the person is already telling their story.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, points (48) and (49), list of examples under the law enforcement exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-gemeentelijke-ai-tekst","legacy_id":"raip:example:example-artikel-50-gemeentelijke-ai-tekst","type":"example","slug":"example-artikel-50-gemeentelijke-ai-tekst","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"607ccc8ecea80ab82aa178e5832f9ad99914406b4a757935b3aabb504f27a676","label":"AI text from a municipality: when a final check counts as editorial control","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-law-body"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 count public administration and public services among the matters of public interest covered by the disclosure duty for published AI text in Article 50(4). For the exception, the guidelines require two cumulative conditions: the AI-generated or manipulated text has undergone human review or editorial control, and a natural or legal person holds editorial responsibility for the publication. They add that human review means a deliberate examination of the substance with fact-checking as a minimum requirement, and that superficial, purely formal or procedural checks such as spell-checking or grammatical correction do not meet it. For the second condition they expect the identity or the function carrying editorial responsibility to be publicly available in an easily findable place.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, point (131), points (133) to (136) and point (138)","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Our reading is that many municipalities do have a publication process but no identifiable person carrying substantive final responsibility, and that is exactly the hinge of this exception. Assign that role explicitly, make it findable who holds it, and record per page who checked the facts, or otherwise take the simpler route and label the text. Watch the order of steps in your workflow, because an AI tool that substantively rewrites the text after human sign-off removes the ground from under that sign-off.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, point (131), points (133) to (136) and point (138)","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-interne-medewerkersassistent","legacy_id":"raip:example:example-artikel-50-interne-medewerkersassistent","type":"example","slug":"example-artikel-50-interne-medewerkersassistent","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"fa7779195063a2d6c102ecd9efa1d0962397eab18f9e9a1016737e97d85efc17","label":"Internal assistant for HR and compliance: an exception with a condition","summary":"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?","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 list an internal employee-facing assistant for properly trained, AI-literate staff who are aware they are using AI systems for internal organisational purposes as an example where the obviousness exception in Article 50(1) applies.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"The guidelines tie this exception explicitly to properly trained, AI-literate staff. Relying on it therefore stands or falls with what you have arranged and can demonstrate on literacy under Article 4: without that record you are leaning on an assumption about your own people.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-klinisch-beslissysteem","legacy_id":"raip:example:example-artikel-50-klinisch-beslissysteem","type":"example","slug":"example-artikel-50-klinisch-beslissysteem","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"373ac7240ce91407219c0e1aa4125e839d59caee1738579bab6fc375dfa28564","label":"Diagnostic support for clinicians: no disclosure duty, still due care","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission guidelines of 20 July 2026 list interactive AI systems intended only for properly trained health professionals to support medical diagnosis and suggest treatments as an example where the obviousness exception in Article 50(1) applies: for that user the AI nature is clear.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"The exception attaches to the user, not to the system. The moment the same model also speaks to patients, that ground falls away and the disclosure duty applies as normal. And an exception to Article 50 says nothing about the rest: where it is a medical device or a high-risk application, those regimes continue to apply in full.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (45), lists with and without the obviousness exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-artikel-50-opsporing-uitzondering","legacy_id":"raip:example:example-artikel-50-opsporing-uitzondering","type":"example","slug":"example-artikel-50-opsporing-uitzondering","version":"1.0.0","effective_at":"2026-07-20T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"f22913de6679b4a1f4549e5f0135681758c39a74b7ce0d57e99aab9ee6bb2603","label":"Law enforcement: exempt from the disclosure duty, with safeguards","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-50-transparency"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 50(1) exempts providers of interactive AI systems from the disclosure duty where they are authorised by law to detect, prevent, investigate or prosecute criminal offences, subject to appropriate safeguards for the rights and freedoms of third parties. The Commission guidelines of 20 July 2026 elaborate that exception.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (46), law-enforcement exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"The exception is narrow and conditional: it requires a legal basis for the law-enforcement task and appropriate safeguards for third parties. A public task by itself is not enough, and the safeguards are not a footnote but part of the ground you are relying on.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-article-50-guidelines","source_locator":"Commission Guidelines C(2026) 5054 final, 20.7.2026, paragraph (46), law-enforcement exception","source_url":"https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-beoordelingssysteem-personeel-incidentmelding","legacy_id":"raip:example:example-beoordelingssysteem-personeel-incidentmelding","type":"example","slug":"example-beoordelingssysteem-personeel-incidentmelding","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"10323fa9be53a342a148727da06c2c2e2326f11f0d1102e4586b4686a08f6df9","label":"Performance scoring for staff goes wrong: report or not","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-73-incident-reporting"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 3, point (49) defines a serious incident as an incident or malfunctioning of an AI system that directly or indirectly leads to, among other things, the infringement of obligations under Union law intended to protect fundamental rights. Article 73(1) places the duty to report serious incidents on the provider of the high-risk AI system placed on the Union market, towards the market surveillance authorities of the Member State where the incident occurred. Article 73(2) requires the report to be made immediately after the provider has established a causal link between the AI system and the serious incident, or the reasonable likelihood of such a link, and in any event no later than 15 days after the provider or, where applicable, the deployer becomes aware of the serious incident, with the period taking account of the severity of the incident. Article 73(6) obliges the provider, after reporting, to perform the necessary investigations without delay in relation to the serious incident and the AI system concerned, including a risk assessment and corrective action, cooperating with the competent authorities and, where relevant, with the notified body concerned. Article 26(5) requires a deployer that has identified a serious incident to inform, immediately, first the provider and then the importer or distributor and the relevant market surveillance authorities.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 3(49), Article 26(5) and Article 73(1), (2) and (6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"In the workplace the hard part is the awareness trigger in Article 73(2): harm spread thinly across many employees does not arrive as an alarm but trickles in through complaints, a performance review or the works council. Whether systematically low scores that feed into promotion and dismissal amount to a serious incident depends, on our reading, on whether obligations protecting fundamental rights have thereby been infringed within the meaning of Article 3, point (49)(c); Article 73 does not make that assessment for you. Decide in advance which HR signal counts as an incident signal and who puts it in front of the provider that same week, rather than reconstructing that afterwards; Article 26(5) also requires you as deployer, once you have identified a serious incident, to inform the provider immediately and then the importer or distributor and the market surveillance authority. Note finally that the corrective action in Article 73(6) concerns the system; what should happen to promotion or dismissal decisions already taken is, on our reading, not answered by this article and runs through other rules, employment law among them.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 3(49), Article 26(5) and Article 73(1), (2) and (6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-biometrische-verificatie-grenspoort","legacy_id":"raip:example:example-biometrische-verificatie-grenspoort","type":"example","slug":"example-biometrische-verificatie-grenspoort","version":"1.0.0","effective_at":"2026-05-19T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"e2f0da23dce88ea57a597d35e2ab6c9bbf07dbbf6bbc17182bbb2203a51e218c","label":"Face comparison at the border gate: verification or identification","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:annex-iii-high-risk"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission draft guidelines of 19 May 2026 state that biometric verification falls outside the high-risk classification: one-to-one comparison of presented biometrics with previously stored biometrics, for the sole purpose of confirming that a person is who they claim to be. Where the same capture is additionally compared against a law-enforcement database, it does become remote biometric identification. The document is a consultation version: non-binding and not yet final.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraph (136)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Test your biometric application on purpose rather than technology: the same camera and the same model stay outside the high-risk route as long as the comparison is one-to-one and only confirms identity, and fall inside it as soon as that same capture is also held against a database. Record per application what the comparison runs against, because that single design choice moves the entire regime.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraph (136)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-ernstig-incident-productielijn-meldtermijn","legacy_id":"raip:example:example-ernstig-incident-productielijn-meldtermijn","type":"example","slug":"example-ernstig-incident-productielijn-meldtermijn","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"ebe7b7020fc00e471f22844f0b97ed3e30f6702e78b04c376cd86720ec3d51bd","label":"Serious incident on the production line: who reports and within what deadline","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-73-incident-reporting"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 73(1) places the reporting of serious incidents on the provider of the high-risk AI system placed on the Union market, addressed to the market surveillance authorities of the Member State where the incident occurred. Article 73(2) requires that report to be made immediately after the provider has established a causal link between the AI system and the serious incident, or the reasonable likelihood of such a link, and in any event no later than 15 days after the provider or, where applicable, the deployer becomes aware of the incident, with the reporting period taking account of the severity of the incident. Article 73(4) shortens that to no later than 10 days in the event of the death of a person. Article 73(6) obliges the provider, following the report, to perform the necessary investigations without delay, including a risk assessment and corrective action, and not to perform any investigation involving alteration of the AI system in a way that may affect the subsequent evaluation of the causes of the incident without first informing the competent authorities. Article 26(5) requires a deployer that has identified a serious incident to inform, immediately, first the provider and then the importer or distributor and the relevant market surveillance authorities, and makes Article 73 apply mutatis mutandis where the deployer cannot reach the provider.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 73(1), (2), (4) and (6), and Article 26(5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 73(2) as meaning that the clock can already start at the deployer on the factory floor, while the report under Article 73(1) sits with you as the provider of the AI system. That does not leave your customer free to sit still: Article 26(5) gives the deployer a notification route of its own, running past you, the importer or distributor and the market surveillance authority, and makes Article 73 apply mutatis mutandis if you cannot be reached. Fix that route in the contract and in the service line before anything happens. Where an operator is seriously injured but does not die, we read the outer limit of Article 73(2) as the one in play rather than the 10 days of Article 73(4); how much sooner than that outer limit you must report is left open by the text and turns on severity. Bear in mind as well that the reflex to repair the machine immediately and resume production can collide with Article 73(6), because an investigation that alters the system first requires notice to the competent authority. Working that out only during the incident costs days that these deadlines do not allow.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 73(1), (2), (4) and (6), and Article 26(5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-fria-bijstandsuitkering-gemeente","legacy_id":"raip:example:example-fria-bijstandsuitkering-gemeente","type":"example","slug":"example-fria-bijstandsuitkering-gemeente","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"563700f6a56bbda6f2fd701e38b2eeb9df22a2523f7612fe1109c68a791ffe42","label":"Awarding social assistance in a municipality: the FRIA and the notification","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-law-body"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-27-fria"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 27(1) requires deployers which are bodies governed by public law, or private entities providing public services, to perform an assessment of the impact on fundamental rights that the use of a high-risk AI system referred to in Article 6(2) may produce, prior to deploying it, with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment covers, among other elements, the categories of natural persons and groups likely to be affected, the specific risks of harm to those categories, the implementation of human oversight measures, and the measures to be taken if those risks materialise, including the arrangements for internal governance and complaint mechanisms. Article 27(3) provides that once the assessment has been performed, the deployer shall notify the market surveillance authority of its results and submit the filled-out template referred to in paragraph 5 as part of that notification, and that in the case referred to in Article 46(1) deployers may be exempt from that obligation to notify. Article 27(5) provides that the AI Office shall develop a template for a questionnaire, including through an automated tool, to facilitate deployers in complying with their obligations under this Article in a simplified manner.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1), (3) and (5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 27 as making the municipality the most obvious deployer here, and as requiring the assessment to be complete before the first application runs through the system, not as an account rendered afterwards. That duty does depend first on whether this system is high-risk at all: does it help decide entitlement to social assistance, or does it stay within a preparatory or narrowly procedural task under Article 6(3), which closes its own exception again once the system profiles citizens? Answer that question before you start on paragraph 1. An entry in the public algorithm register is on our reading something different from the assessment under paragraph 1, and it does not replace notifying the market surveillance authority of the results. In practice it pays to record the citizen's complaint route and the case worker's room to depart from the signal in the same file, because paragraph 1 asks for precisely those two elements.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1), (3) and (5)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-fria-recidiverisico-politie","legacy_id":"raip:example:example-fria-recidiverisico-politie","type":"example","slug":"example-fria-recidiverisico-politie","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"3136fdfa784bcee684810eafa3fc784e9ff1905adf38d86767b1e6daed7c37da","label":"Recidivism scoring in police work: when the assessment must be redone","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-law-body"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-27-fria"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 27(1) requires deployers which are bodies governed by public law to perform, prior to deploying a high-risk AI system referred to in Article 6(2), an assessment of the impact on fundamental rights that its use may produce, with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment consists of a description of the processes in which the system will be used, of the period and frequency of use, of the categories of natural persons and groups likely to be affected, of the specific risks of harm taking into account the information given by the provider pursuant to Article 13, of the implementation of human oversight measures according to the instructions for use, and of the measures to be taken if those risks materialise. Article 27(2) provides that the obligation applies to the first use, that previously conducted impact assessments or existing assessments carried out by the provider may be relied on in similar cases, and that a deployer who considers during use that any element listed in paragraph 1 has changed or is no longer up to date shall take the necessary steps to update the information. Article 27(3) provides that the results are notified to the market surveillance authority together with the filled-out template, and that in the case referred to in Article 46(1) an exemption from that notification duty may apply.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 27 as covering a police service as a body governed by public law under paragraph 1, and as an assessment that does not stop at first use: retraining on newer investigative data or extending use to a second region touches the elements of paragraph 1 and, on our reading, calls for updating the record. That retraining also raises a question Article 27 itself does not answer, namely whether the change goes far enough to count as a substantial modification, which would make you a provider in your own right under Article 25. Bear in mind as well that the exception in paragraph 3 concerns, on our reading, the notification and not the assessment itself. Finally, start that assessment only after establishing that the deployment as such is permitted, because Article 5 rules out certain predictive applications in criminal investigation.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-fria-selectie-inschrijving-hogeschool","legacy_id":"raip:example:example-fria-selectie-inschrijving-hogeschool","type":"example","slug":"example-fria-selectie-inschrijving-hogeschool","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"81f378ec11bfd5271d13fa170d8d63f220e78317143a657af062421c1512c069","label":"Selection at student admission: a DPIA is not yet a FRIA","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-service-provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-27-fria"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 27(1) provides that deployers which are bodies governed by public law, or private entities providing public services, and deployers of high-risk AI systems referred to in points 5(b) and (c) of Annex III, shall perform an assessment of the impact on fundamental rights prior to deploying a high-risk AI system referred to in Article 6(2), with the exception of systems intended to be used in the area listed in point 2 of Annex III. That assessment consists of a description of the deployer's processes in which the system will be used in line with its intended purpose, of the period and frequency of use, of the categories of natural persons and groups likely to be affected, of the specific risks of harm to those categories, of the implementation of human oversight measures, and of the measures to be taken if those risks materialise, including the arrangements for internal governance and complaint mechanisms. Article 27(2) provides that the obligation applies to the first use, that the deployer may in similar cases rely on previously conducted impact assessments or existing assessments carried out by the provider, and that a deployer who considers during use that any element listed in paragraph 1 has changed or is no longer up to date shall take the necessary steps to update the information. Article 27(4) provides that where an obligation under this Article is already met through the data protection impact assessment conducted pursuant to Article 35 of Regulation (EU) 2016/679 or Article 27 of Directive (EU) 2016/680, the assessment under paragraph 1 complements that data protection impact assessment.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1), (2) and (4)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 27 as placing the education institution that runs this selection itself in the deployer role, but that alone does not settle the duty. Paragraph 1 names bodies governed by public law and private entities providing public services, and whether a state-funded or a private university of applied sciences answers to either description is the question you have to settle first. If it does, an existing data protection impact assessment is on our reading the starting point rather than the last word: paragraph 4 has the fundamental rights assessment sit alongside it, and since Regulation (EU) 2026/1744 that assessment may incorporate or cross-refer to relevant parts of it, so the real question is which elements of paragraph 1 are still missing. For you that means recording which groups of students may be affected, how the admissions committee or the teacher can correct an outcome, and where a rejected applicant can lodge a complaint. If the selection rule or the assessment component underpinning the ranking changes, that is on our reading the moment to update the record, rather than the start of the next academic year.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 27(1), (2) and (4)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-fria-zorgverzekeraar-risicobeoordeling","legacy_id":"raip:example:example-fria-zorgverzekeraar-risicobeoordeling","type":"example","slug":"example-fria-zorgverzekeraar-risicobeoordeling","version":"1.0.0","effective_at":"2026-05-19T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"da4c33f856bb8621e0a1ecad5647b97f9511b86d1c9cc729d555166238398e7e","label":"Health insurer using AI for risk assessment: public or private makes no difference","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-27-fria"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission draft guidelines of 19 May 2026 state that the health and life insurance in point 5(c) may be offered on a private or a public basis: a health insurer governed by public law also falls within it, so long as the system is intended for risk assessment or pricing with regard to natural persons. Privately serviced health insurance counts as an essential private service, even in a Member State with a public healthcare system. Unlike point 5(b), point 5(c) provides no exception for fraud detection. The document is a consultation version: non-binding and not yet final.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraphs (319) to (321)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"For the FRIA question point 5(c) counts twice: it makes the system high-risk and it makes you, as deployer, one of the parties Article 27 names. A public-law form or a public healthcare system in your Member State changes nothing there. Do not count on the fraud-detection exception from point 5(b) either: it does not apply here, and a fraud feature alongside risk assessment does not take the system out.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraphs (319) to (321)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-logging-hogeschool-aanmelding-toetsing","legacy_id":"raip:example:example-logging-hogeschool-aanmelding-toetsing","type":"example","slug":"example-logging-hogeschool-aanmelding-toetsing","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"87cc04e5994d67a08654e7bda8c7ea6eb508508cf029630e0faf71818cbd70bb","label":"Logging in admission and assessment: what the institution keeps in its own hands","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-12-logging"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 12(1) requires a high-risk AI system to technically allow for the automatic recording of events (logs) over the lifetime of the system, and Article 12(2) ties that recording to a level of traceability appropriate to the intended purpose of the system. Article 26(6) provides that deployers keep the automatically generated logs to the extent those logs are under their control, for a period appropriate to the intended purpose, of at least six months, unless applicable Union or national law provides otherwise, in particular Union law on the protection of personal data. Article 19(1) places a corresponding retention duty on providers for the logs generated by their systems that are under their control, likewise for at least six months.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2), Article 19(1) and Article 26(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"On our reading, the fact that the supplier hosts the logs does not by itself place the school outside Article 26(6): what matters is whether the logs are under its control. The Regulation does not define that notion, and on our reading server location is not decisive in itself; the question is whether you can actually and contractually dispose of those logs, and it has to be answered for each procurement. So agree at procurement that the institution can request, export and itself retain the logs for the chosen period, and record which admission and assessment events that covers. Take data protection into account at the same time, because the same provision allows other Union or national law to cap the retention period.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2), Article 19(1) and Article 26(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-logging-productielijn-veiligheidscomponent","legacy_id":"raip:example:example-logging-productielijn-veiligheidscomponent","type":"example","slug":"example-logging-productielijn-veiligheidscomponent","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"624d417df4b70bdd1072de6b663f188dd48de9556bd5866eabfc9f1da5fe3d3a","label":"Logging on the production line: which logs the manufacturer keeps and which the factory keeps","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-12-logging"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 12(1) requires high-risk AI systems to technically allow for the automatic recording of events (logs) over the lifetime of the system. Under Article 12(2), those logging capabilities must enable the recording of events relevant for identifying situations that may result in the system presenting a risk within the meaning of Article 79(1) or in a substantial modification, for facilitating the post-market monitoring referred to in Article 72, and for monitoring the operation of the high-risk AI systems referred to in Article 26(5). Article 19(1) obliges providers to keep those automatically generated logs to the extent they are under their control, for a period appropriate to the intended purpose of the system, of at least six months, unless applicable Union or national law provides otherwise. Article 26(6) places a corresponding retention duty on deployers for the logs under their control, with the same six-month floor.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2), Article 19(1) and Article 26(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 19 together with Article 26(6) as leaving the manufacturer, acting here as the provider, and the factory, acting as the deployer, each with a retention duty of its own for the logs under its control. The Regulation does not say when logs count as being under your control, and on our reading the place where the recording physically lands does not settle that by itself; the question has to be answered system by system. For a production line that means recording which events stay in the machinery controller and which travel to the supplier, and securing access to that second set contractually before you need it. Leave that unarranged and you risk being unable to trace a line stoppage or a quality control rejection back to the behaviour of the system.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2), Article 19(1) and Article 26(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-logging-taakverdeling-werkvloer","legacy_id":"raip:example:example-logging-taakverdeling-werkvloer","type":"example","slug":"example-logging-taakverdeling-werkvloer","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"be80038e3b50070a20f07e501c8a3a9c35fff975654135c454e9a4c5a9b96545","label":"Logging in task allocation at work: evidence about the system or a file on the employee","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-12-logging"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 12(1) requires high-risk AI systems to technically allow for the automatic recording of events (logs) over the lifetime of the system. Article 12(2)(c) names, as one of the purposes of those logging capabilities, monitoring the operation of the systems referred to in Article 26(5), and that paragraph obliges deployers to monitor operation on the basis of the instructions for use and, where relevant, to inform the provider in accordance with Article 72. Article 26(6) obliges deployers to keep the automatically generated logs to the extent they are under their control, for a period appropriate to the intended purpose, of at least six months, unless applicable Union or national law provides otherwise. Article 19(1) sets out a corresponding retention duty for providers, with the same six-month floor.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2)(c), Article 19(1) and Article 26(5) and (6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 12(2) as putting the logging capabilities there first of all to follow risks, substantial modifications and the operation of the system, not to sharpen judgements about individual staff. Whether that statement of purpose also limits what the retained logs may later be used for is something Article 12 does not say: on our reading that limit has to come from data protection law, to which Article 26(6) itself refers. For HR a practical line follows: keep what is needed to trace an outcome and to carry out the monitoring under Article 26(5), and settle in writing beforehand whether those same files may double as a performance record. Bear in mind that the six-month floor can be shorter than the period within which an employee challenges a promotion decision; the Regulation does not govern that evidential position.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 12(1) and (2)(c), Article 19(1) and Article 26(5) and (6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-politie-model-bijtrainen-opsporing","legacy_id":"raip:example:example-politie-model-bijtrainen-opsporing","type":"example","slug":"example-politie-model-bijtrainen-opsporing","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"f76e99119daefd7c5cfc07f8db92500ea93ce6a13b3e86aff47818a0a15cb89a","label":"Police fine-tuning a model for investigations: settle the role question first","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:gpai-model-provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-53-gpai"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 53(1) requires providers of general-purpose AI models to do four things: draw up and keep up-to-date the technical documentation of the model, including its training and testing process and the results of its evaluation, containing at a minimum the information set out in Annex XI so that it can be provided on request to the AI Office and the national competent authorities; draw up, keep up-to-date and make available information and documentation to providers of AI systems who intend to integrate the model, containing at a minimum the elements set out in Annex XII; put in place a policy to comply with Union law on copyright and related rights; and draw up and make publicly available a sufficiently detailed summary about the content used for training, according to a template provided by the AI Office. Article 53(2) provides that the obligations under points (a) and (b) do not apply to providers of models released under a free and open-source licence that allows for the access, usage, modification and distribution of the model, and whose parameters, including the weights, the information on the model architecture and the information on model usage, are made publicly available, and that this exception does not apply to general-purpose AI models with systemic risks.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 53(1) and (2)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Start with your role, because merely using someone else's model does not by itself make you a model provider; Article 53 does not say at what point a service that fine-tunes crosses that line, and the point deserves an explicit answer rather than an assumption. A further question comes before Article 53: whether your fine-tuned investigation model has been placed on the market within the meaning of the Regulation. That it never leaves the service does not settle this on its own, since the Commission's guidelines on the scope of the obligations for providers of general-purpose AI models also treat internal use affecting the rights of natural persons as placing on the market. We read paragraph 2 as attaching the open-source exception to your own model rather than carrying it over automatically from the model you started out with: if you do not publish the weights and architecture of the fine-tuned model, which is the obvious course in criminal matters, then on that reading the exception is not open to you, and the duties under points (c) and (d) remain in place in any event, since paragraph 2 lifts only points (a) and (b). So record, version by version, exactly what you changed, on which data and to what end, because that record is what decides whether you stand here as a user or as a provider.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 53(1) and (2)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-post-market-monitoring-hr-beoordelingssysteem","legacy_id":"raip:example:example-post-market-monitoring-hr-beoordelingssysteem","type":"example","slug":"example-post-market-monitoring-hr-beoordelingssysteem","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"834c3d7f217e8710ac5698e4b555f10d6b66e59eddbb6facca765496ad4b791d","label":"Assessing staff: signals from the workplace flowing back to the provider","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-72-post-market-monitoring"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 72(1) requires providers to establish and document a post-market monitoring system in a manner proportionate to the nature of the AI technologies and the risks of the high-risk AI system. Article 72(2) requires that system to actively and systematically collect, document and analyse relevant data, which may be provided by deployers or collected through other sources, on performance throughout the system's lifetime, allowing the provider to evaluate continuous compliance with the requirements set out in Chapter III, Section 2. Article 72(3) provides that the system is based on a plan forming part of the technical documentation referred to in Annex IV, and instructs the Commission to adopt an implementing act laying down detailed provisions establishing a template for that plan and the list of elements to be included in it; the Regulation sets 2 February 2026 as the date for that act.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Our reading is that use which departs from what was intended is exactly the kind of real-world data paragraph 2 has in mind, because it bears on the human oversight and the intended purpose laid down in Section 2. Paragraph 3 points to a Commission template for which the Regulation sets 2 February 2026 as the date; check whether that template has since been adopted and align with it if so, since paragraph 4 refers to it as well. In the meantime we would not put the design of your monitoring on hold for it: which data you need already follows from the requirements the system must continue to meet. Agree with the organisations using the system on the route by which employee complaints and deviations in assessments reach you.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-post-market-monitoring-proctoring-tentamens","legacy_id":"raip:example:example-post-market-monitoring-proctoring-tentamens","type":"example","slug":"example-post-market-monitoring-proctoring-tentamens","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"683c18d4fac97ff4db4363416589f34e9615625ed4323784e0b292b8d25ad231","label":"Proctoring during exams: which real-world data the institution reports back","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-72-post-market-monitoring"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 72(1) requires providers to establish and document a post-market monitoring system, proportionate to the nature of the AI technologies and the risks of the system. Article 72(2) provides that this system actively and systematically collects, documents and analyses relevant data which may be provided by deployers or collected through other sources on the performance of high-risk AI systems throughout their lifetime, and which allow the provider to evaluate the continuous compliance of those systems with the requirements set out in Chapter III, Section 2. Where relevant, monitoring includes an analysis of the interaction with other AI systems. Article 72(3) provides that the system is based on a plan forming part of the technical documentation referred to in Annex IV.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"The duty sits with the provider, but the useful signals arise in education itself: unfounded suspicions, student complaints, differences between programmes and assessment formats. Our reading is that paragraph 2 allows deployers to supply that data while leaving the duty to collect and analyse it with the provider. What the article does not settle is the route by which the data reaches you, or whether the institutions are bound to supply it; that is something you have to arrange with them and cannot read out of Article 72. We would therefore fix that route before the system goes live, and would also set out in the plan how you break performance down by assessment format and by group, since an average across all schools hides precisely the pattern you are looking for.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1)-(3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-post-market-monitoring-veiligheidscomponent-productielijn","legacy_id":"raip:example:example-post-market-monitoring-veiligheidscomponent-productielijn","type":"example","slug":"example-post-market-monitoring-veiligheidscomponent-productielijn","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"b1ed8bb9e9486b8528de2cce401e4f0ce0bed59bd284ede4a90ac5703f85dc09","label":"Safety component on the production line: the manufacturer keeps watching after delivery","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-72-post-market-monitoring"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 72(1) requires providers to establish and document a post-market monitoring system in a manner proportionate to the nature of the AI technologies and the risks of the high-risk AI system. Article 72(3) provides that this system is based on a post-market monitoring plan, and that the plan forms part of the technical documentation referred to in Annex IV. Article 72(4) gives providers of high-risk AI systems covered by the Union harmonisation legislation listed in Section A of Annex I, where a post-market monitoring system and plan are already established under that legislation, the choice of integrating the necessary elements described in paragraphs 1, 2 and 3 into those systems and plans, using the template referred to in paragraph 3, provided this achieves an equivalent level of protection.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1), (3) and (4)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Our reading is that Article 72 does not force a second, separate monitoring structure here: paragraph 4 gives you the choice of housing the elements in the system and plan you already run under product legislation, using the template from paragraph 3 and as long as the level of protection remains equivalent. That route stands or falls on a question the article does not answer for you: does the applicable product legislation genuinely have an established post-market monitoring system and plan, or do you run quality control and incident follow-up that were never established as such. Beyond that, you need to be able to show that the signals coming off the factory floor genuinely speak to continued compliance with the high-risk AI requirements, and not only to the mechanical safety of the machine. We would therefore record, for each production signal, which requirement it touches, so that integration does not become dilution.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 72(1), (3) and (4)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-proctoring-tentamen-incidentmelding","legacy_id":"raip:example:example-proctoring-tentamen-incidentmelding","type":"example","slug":"example-proctoring-tentamen-incidentmelding","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"9acab6c994b68ac7110c3307f8907ee18580a895ef51f8ed17588a3e03b73504","label":"Proctoring during an exam overshoots: from signal to reporting duty","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-73-incident-reporting"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 3, point (49) defines a serious incident as an incident or malfunctioning of an AI system that directly or indirectly leads to, among other things, the infringement of obligations under Union law intended to protect fundamental rights. Article 73(1) obliges the provider of a high-risk AI system placed on the Union market to report serious incidents to the market surveillance authorities of the Member State where the incident occurred. Article 73(2) requires the report to be made immediately after the provider has established the causal link or the reasonable likelihood of such a link, and in any event no later than 15 days after the provider or, where applicable, the deployer becomes aware of the serious incident. Article 73(5) allows an initial, incomplete report followed by a complete report where this is necessary to ensure timely reporting. Article 73(7) provides that upon receiving a report concerning a serious incident as referred to in Article 3, point (49)(c), the market surveillance authority informs the national public authorities or bodies referred to in Article 77(1). Article 26(5) requires a deployer that has identified a serious incident to inform, immediately, first the provider and then the importer or distributor and the relevant market surveillance authorities.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 3(49), Article 26(5) and Article 73(1), (2), (5) and (7)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 73 as meaning that an incident in education is rarely a single dramatic moment: the signal sits in the pattern in exam results, and the institution sees that pattern before the software supplier does. Whether that pattern amounts to a serious incident is a separate question: it turns on whether the wrongful flagging and the withdrawal of grades count as an infringement of obligations protecting fundamental rights within the meaning of Article 3, point (49)(c), and Article 73 does not make that assessment for you. Leaving the question open until your own investigation is finished carries a risk, because the Article 73(2) period runs from awareness. Set up your examination and complaints process so that such a pattern reaches the provider within days; Article 26(5) also puts a notification duty on the institution itself as deployer, towards the provider, the importer or distributor and the market surveillance authority. The initial, incomplete report of Article 73(5) is the pressure valve here; waiting for a finished investigation report is not.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 3(49), Article 26(5) and Article 73(1), (2), (5) and (7)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-securitymonitoring-kritieke-digitale-infrastructuur","legacy_id":"raip:example:example-securitymonitoring-kritieke-digitale-infrastructuur","type":"example","slug":"example-securitymonitoring-kritieke-digitale-infrastructuur","version":"1.0.0","effective_at":"2026-05-19T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"8dd8de28fda79bd209141410f49769cdb9af6a28ce4f10a431b5106d06367115","label":"Security monitoring by a SaaS company: cybersecurity alone is not a safety component","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:annex-iii-high-risk"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission draft guidelines of 19 May 2026 state that point 2 of Annex III lists AI systems intended to be used as safety components in the management and operation of, among other things, critical digital infrastructure, and that Recital 55 draws a clear distinction between a safety component and a cybersecurity component. To fall within point 2, an AI system must not be used solely for cybersecurity purposes; without a direct safety role it cannot be a safety component in critical infrastructure and therefore cannot be classified as high-risk under Article 6(2). As examples of systems used solely for cybersecurity and thus falling outside point 2, they list an AI honeypot that identifies and neutralises cyber threats in real time, technology that actively engages with potential attackers to learn new attack patterns, a system supporting the detection of unauthorised access, and a system that detects suspicious email addresses and identifies stolen data. They add that an AI system is classified as a safety component in critical infrastructure only where it is used by an entity identified as a critical entity by a Member State under the CER Directive, and that this interpretation does not require that status to be disclosed to a third-party provider. The document is a consultation version: non-binding and not yet final.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on the classification of high-risk AI systems, 19 May 2026, annex on Annex III, paragraphs (187), (188), (190) and (191)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read this passage as a line drawn function by function rather than customer by customer: the fact that your client operates critical digital infrastructure does not by itself turn your monitoring product into a safety component. What we cannot settle from the text is where mere flagging ends and a safety function begins, because the same guidelines also count monitoring and detecting situations that may directly lead to physical harm among the safety functions. The open question is therefore whether your SaaS platform stands apart from the systems that drive physical control, or in practice becomes part of them. So record, per product function, what the system performs and what it expressly leaves to the operator, because that distinction carries your entire classification and is hard to reconstruct after the fact.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on the classification of high-risk AI systems, 19 May 2026, annex on Annex III, paragraphs (187), (188), (190) and (191)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-toelating-hogeschool-registratie-eu-databank","legacy_id":"raip:example:example-toelating-hogeschool-registratie-eu-databank","type":"example","slug":"example-toelating-hogeschool-registratie-eu-databank","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"3d53527e1fe64875c7712e50282816d365b64c9b924e3a6965b22cf6c50a7dff","label":"Admission system at a higher education institution: who registers in the EU database","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:public-law-body"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:conformity-ce-registration"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 43(2) provides that for high-risk AI systems referred to in points 2 to 8 of Annex III, providers follow the conformity assessment procedure based on internal control set out in Annex VI, which does not provide for the involvement of a notified body. Article 47(1) requires the provider to draw up, for each high-risk AI system, a written machine-readable, physical or electronically signed EU declaration of conformity and to keep it at the disposal of the national competent authorities for ten years after the system has been placed on the market or put into service. Article 49(1) requires the provider or, where applicable, the authorised representative to register themselves and their system in the EU database referred to in Article 71 before an Annex III high-risk system is placed on the market or put into service, with the exception of the systems listed in point 2 of Annex III. Article 49(3) provides that deployers that are public authorities or Union institutions, bodies, offices or agencies, or persons acting on their behalf, register themselves, select the system and register its use in that same database before putting such a system into service or using it, again with the exception of the systems listed in point 2 of Annex III.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 43(2), Article 47(1) and Article 49(1) and (3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"We read Article 49 as two separate registrations: paragraph 1 covers the provider and its system, paragraph 3 covers your own use, and on that reading the first does not relieve you of the second. The hinge question for the institution is therefore not whether the supplier does its job, but whether it is a public authority within the meaning of paragraph 3. The Regulation does not define that term, and until that is settled it remains open whether the institution must itself appear in the EU database. In practice we would move the request for the EU declaration of conformity and the registration number into the procurement stage, so that the question does not turn into a blocker just before an application period opens.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 43(2), Article 47(1) and Article 49(1) and (3)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-triage-spoedeisende-hulp","legacy_id":"raip:example:example-triage-spoedeisende-hulp","type":"example","slug":"example-triage-spoedeisende-hulp","version":"1.0.0","effective_at":"2026-05-19T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"62d16510c753780018aa9840884d802dfdc1b68e969a525fc0d205fdd486c3f3","label":"Emergency triage system: two routes to high risk","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:annex-iii-high-risk"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"The Commission draft guidelines of 19 May 2026 state that an emergency healthcare patient triage system may qualify as a medical device; where it then meets Article 6(1), it is high-risk via Annex I and sectoral rules apply. A triage system that is not a medical device is high-risk via Article 6(2) and point 5(d) of Annex III. The document is a consultation version: non-binding and not yet final.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraphs (334) and (335)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"First establish whether your triage system is a medical device, because that question decides the whole route: via Annex I the assessment travels with the medical conformity assessment and its date, via Annex III the separate timeline of Article 6(2) applies. Either way the outcome is high-risk, so postponing that determination only creates uncertainty about which regime to set up.","citations":[{"source_id":"praxikon:eu:ai-act:source:commission-draft-high-risk-classification-guidelines","source_locator":"Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraphs (334) and (335)","source_url":"https://digital-strategy.ec.europa.eu/en/library/draft-commission-guidelines-classification-high-risk-ai-systems","eli":null}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"guidance","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:example:example-webshop-kredietcheck-technisch-dossier","legacy_id":"raip:example:example-webshop-kredietcheck-technisch-dossier","type":"example","slug":"example-webshop-kredietcheck-technisch-dossier","version":"1.0.0","effective_at":"2024-08-01T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"d78a5fb746a80b877e3392af6e2f5baa03e844e1777db2c3b305ccb327eb0704","label":"Webshop builds its own consumer credit check: what belongs in the file","summary":"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.","topics":["examples"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":[],"affected_actor_ids":[],"oversight_actor_ids":[],"evidence_owner_ids":[],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":["praxikon:eu:ai-act:obligation:article-11-technical-documentation"],"change_ids":[],"action_ids":[],"evidence_ids":[],"control_ids":[],"template_ids":[],"conditions":[],"exceptions":[],"statements":[{"kind":"official_fact","text":"Article 11(1) requires the technical documentation of a high-risk AI system to be drawn up before that system is placed on the market or put into service, and to be kept up to date. It must be drawn up so as to demonstrate compliance with the requirements of that Section and to give national competent authorities and notified bodies the necessary information, in a clear and comprehensive form, to assess that compliance; it contains at a minimum the elements set out in Annex IV. Annex IV lists those elements as applicable to the AI system concerned, and asks among other things for the methods and steps performed for development, including, where relevant, recourse to pre-trained systems or tools provided by third parties and how those were used, integrated or modified by the provider, for the validation and testing procedures used, and for a description of relevant changes made by the provider to the system through its lifecycle. SMEs, including start-ups, may supply those elements in a simplified manner; where they take that route, they must use the simplified form the Commission is to establish for that purpose.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 11(1) and Annex IV","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"Article 11 puts the documentation duty on the provider, so the first open question here is who that provider is. If the creditworthiness assessment of consumers counts as high-risk in your situation, then in our reading there is a strong case that a webshop assembling the system itself and putting it into service under its own name is no longer merely a deployer but ends up on the provider side, carrying the documentation duty that comes with it. Article 11 does not settle that allocation of roles, so test it against the definitions and against Article 25 before assuming the file is your vendor's problem. In our reading the hard part in retail is not the first version of the file but the pace afterwards, because a webshop often ships changes per release while the file stays frozen at the state it had when the feature went into service. So contract for the provenance, training and testing information that Annex IV expects from you when you buy the pre-trained model, and assign per release who updates the file.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 11(1) and Annex IV","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"editorial"}}],"legal_status":"editorial","deadline_at":null,"links":[],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"},"source_review":{"level":"source_verified","last_checked_at":"2026-08-08T00:00:00.000Z","next_review_due_at":"2027-02-04T00:00:00.000Z","date_basis":"first_publication"}},{"id":"praxikon:eu:ai-act:obligation:annex-iii-high-risk","legacy_id":"raip:obligation:annex-iii-high-risk","type":"obligation","slug":"annex-iii-high-risk","version":"1.0.0","effective_at":"2026-07-27T00:00:00.000Z","known_at":"2026-08-08T00:00:00.000Z","valid_until":null,"payload_hash_sha256":"c0afd1789ed393ba3f9ce04205bd74b4831ff0fd58146108fdd8dd08d2f4f6c9","label":"Annex III: high-risk AI","summary":"Classification route for standalone high-risk AI systems under Article 6(2) and Annex III.","topics":["high-risk"],"actor_ids":["praxikon:eu:ai-act:actor:deployer","praxikon:eu:ai-act:actor:provider"],"duty_holder_ids":["praxikon:eu:ai-act:actor:provider"],"affected_actor_ids":["praxikon:eu:ai-act:actor:deployer"],"oversight_actor_ids":[],"evidence_owner_ids":["praxikon:eu:ai-act:actor:provider"],"duty_holder_uncertainty_status":null,"interpretation_status":null,"interpretation_note":null,"obligation_ids":[],"change_ids":[],"action_ids":["praxikon:eu:ai-act:action:annex-iii-classify"],"evidence_ids":["praxikon:eu:ai-act:evidence:annex-iii-classification-record"],"control_ids":["praxikon:eu:ai-act:control:annex-iii-change-trigger"],"template_ids":["praxikon:eu:ai-act:template:annex-iii-classifier"],"conditions":[{"id":"annex-iii-listed-purpose","operator":"all","description":"The intended purpose falls within a use case listed in Annex III."},{"id":"article-6-2-route","operator":"all","description":"Classification follows Article 6(2)."}],"exceptions":[{"id":"article-6-3-exception","operator":"not","description":"A listed Annex III system may fall outside high-risk under the strict conditions in Article 6(3), except where it profiles. The assessment and registration must be documented."}],"statements":[{"kind":"official_fact","text":"The core rules in Chapter III, Sections 1 to 3, for systems under Article 6(2) and Annex III become applicable on 2 December 2027.","citations":[{"source_id":"praxikon:eu:ai-act:source:reg-eu-2026-1744","source_locator":"Amended Article 113, Article 6(2) and Annex III application date","source_url":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","eli":"http://data.europa.eu/eli/reg/2026/1744/oj"}],"review":{"reviewed_at":"2026-08-08T00:00:00.000Z","reviewer":"Praxikon release validation","review_method":"source_link_and_rule_validation","legal_status":"source_checked"}},{"kind":"editorial_interpretation","text":"The later application date does not remove the classification question. 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