{"answer_id":"praxikon:eu:ai-act:answer:trustworthy-ai-altai","canonical_page":"https://www.praxikon.com/en/antwoord/trustworthy-ai-altai","query":"Is the ALTAI / trustworthy AI assessment mandatory under the AI Act?","lang":"en","view":"full","mode":"scenario","question":"Is the ALTAI / trustworthy AI assessment mandatory under the AI Act?","situation":"You know the Assessment List for Trustworthy AI and wonder how it relates to the legal duties.","likely_role":"Provider and deployer alike","note":"The ALTAI from the High-Level Expert Group is a voluntary self-assessment instrument, not a legal duty. It remains useful as a thinking framework and training material, but it does not replace an Article 5 screening, classification or FRIA. Use it as a tool within your Article 4 measures, not as proof of legal compliance.","matched_terms":[],"dataset":{"id":"praxikon:sys:registry:dataset:ai-act-implementation-graph","version":"2.2.0","schema_version":"1.5.0","effective_at":"2026-08-08T00:00:00.000Z","known_at":"2026-09-06T00:00:00.000Z","last_reviewed_at":"2026-08-08T00:00:00.000Z","licence":"https://www.praxikon.com/nl/legal/terms","canonical_url":"https://www.praxikon.com/api/v1/entities"},"obligations":[{"slug":"article-4-ai-literacy","label":"Article 4: AI literacy","summary":"Providers and deployers take measures that support the development of AI literacy.","legal_status":"applicable","deadline_at":"2025-02-02T00:00:00.000Z","high_risk_regime_from":null,"human_page":"https://www.praxikon.com/en/verplichtingen/article-4-ai-literacy","api":"https://www.praxikon.com/api/v1/obligations?lang=en","official_source":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","citations":[{"kind":"official_fact","statement":"Since 27 July 2026, providers and deployers must take measures supporting the development of AI literacy. The provision does not require a guaranteed individual level.","source_id":"praxikon:eu:ai-act:source:reg-eu-2026-1744","source_locator":"Amendment of Article 4; entry into force 27 July 2026","source_url":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","eli":"http://data.europa.eu/eli/reg/2026/1744/oj"},{"kind":"editorial_interpretation","statement":"Evidence is primarily a proportionate record of measures by role and context, not one prescribed course or certificate.","source_id":"praxikon:eu:ai-act:source:commission-ai-literacy-qa","source_locator":"Questions on measures, formats, certificates and records","source_url":"https://digital-strategy.ec.europa.eu/en/faqs/ai-literacy-questions-answers","eli":null},{"kind":"recommended_action","statement":"Inventory roles and AI systems, select appropriate measures and record the choice, implementation and periodic review.","source_id":"praxikon:eu:ai-act:source:commission-ai-literacy-qa","source_locator":"Implementation examples and evidence guidance","source_url":"https://digital-strategy.ec.europa.eu/en/faqs/ai-literacy-questions-answers","eli":null}]},{"slug":"annex-iii-high-risk","label":"Annex III: high-risk AI","summary":"Classification route for standalone high-risk AI systems under Article 6(2) and Annex III.","legal_status":"upcoming","deadline_at":"2027-12-02T00:00:00.000Z","high_risk_regime_from":null,"human_page":"https://www.praxikon.com/en/verplichtingen/annex-iii-high-risk","api":"https://www.praxikon.com/api/v1/obligations?lang=en","official_source":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","citations":[{"kind":"official_fact","statement":"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.","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"},{"kind":"editorial_interpretation","statement":"The later application date does not remove the classification question. An early classification record avoids design and procurement decisions without evidence.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 6 and Annex III","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"recommended_action","statement":"Document now the intended purpose, Annex III point, Article 6(3) assessment, profiling and selected registration path.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 6(2)-(4), Article 49 and Annex III","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}]},{"slug":"article-26-deployer-obligations","label":"Article 26: obligations of deployers of high-risk AI systems","summary":"Twelve paragraphs governing day-to-day use: use in line with the instructions, human oversight by competent people, input data, monitoring and notification, log retention, informing workers before deployment, registration by public authorities and informing the people about whom decisions are made.","legal_status":"upcoming","deadline_at":"2027-12-02T00:00:00.000Z","high_risk_regime_from":null,"human_page":"https://www.praxikon.com/en/verplichtingen/article-26-deployer-obligations","api":"https://www.praxikon.com/api/v1/obligations?lang=en","official_source":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","citations":[{"kind":"official_fact","statement":"Article 26 imposes twelve paragraphs on deployers of high-risk AI systems. Paragraph 1 requires appropriate technical and organisational measures to ensure use in accordance with the accompanying instructions for use. Paragraph 2 requires assigning human oversight to natural persons who have the necessary competence, training and authority, as well as the necessary support. Paragraph 3 leaves other obligations and the freedom to organise one's own resources unaffected. Paragraph 4 requires, to the extent the deployer exercises control over the input data, that such data is relevant and sufficiently representative in view of the intended purpose. Paragraph 5 requires monitoring on the basis of the instructions for use and informing the provider in accordance with Article 72; where there is reason to consider that use may result in a risk within the meaning of Article 79(1), the deployer shall without undue delay inform the provider or distributor and the relevant market surveillance authority and suspend use, and upon identifying a serious incident shall immediately inform first the provider and then the importer or distributor and the market surveillance authorities. Paragraph 6 requires keeping the automatically generated logs under the deployer's control for a period appropriate to the intended purpose and of at least six months, unless Union or national law provides otherwise. Paragraph 7 requires deployers who are employers to inform workers' representatives and the affected workers, before putting into service or using the system at the workplace, that they will be subject to its use. Paragraph 8 imposes the registration obligations of Article 49 on public authorities and Union institutions, bodies, offices and agencies and prohibits use of a system not registered in the EU database referred to in Article 71. Paragraph 9 links the information provided under Article 13 to the data protection impact assessment under Article 35 of Regulation (EU) 2016/679. Paragraph 10 sets additional conditions for post-remote biometric identification in law enforcement. Paragraph 11 opens with the words without prejudice to Article 50 of this Regulation and requires deployers of Annex III systems that make or assist in making decisions related to natural persons to inform those persons that they are subject to the use of the system; for high-risk AI systems used for law enforcement purposes Article 13 of Directive (EU) 2016/680 applies. The transparency obligations of Article 50 have applied since 2 August 2026 and are separate from the date on which paragraph 11 starts to apply. Paragraph 12 requires cooperation with the competent authorities.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 26(1)-(12)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"editorial_interpretation","statement":"The 2 December 2027 date invites postponement, but two elements are preparation work today. Paragraph 7 requires you to inform workers' representatives and the affected workers before the system is put into service at the workplace, and that information is provided, where applicable, in line with existing rules and practice on informing workers. That touches employee participation, and such a process takes months rather than weeks in practice, so a system that must go live in 2027 is discussed in 2026. Paragraph 2 also connects to the human oversight that Article 14 imposes on system design: you must designate natural persons with competence, training, authority and support. That is emphatically not the same as the measures obligation in Article 4. Article 4 requires measures supporting AI literacy and does not require you to guarantee a particular level for individuals; Article 26(2) requires identifiable overseers with a mandate. Conflating the two leaves you believing a generic e-learning is enough while still having no overseer with room to decide. A third underestimated element is paragraph 11: informing the people about whom an Annex III system makes or helps make decisions is visible customer or candidate communication that you have to design across your own organisation.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 26(1)-(12)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"recommended_action","statement":"Draw up now a list of the systems likely to qualify as high-risk from 2 December 2027 and add three columns: who exercises human oversight and with what mandate, when you will inform the works council and the affected workers, and how the persons concerned will receive the notice under paragraph 11. Plan the employee participation process a year ahead.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 26(1)-(12)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}]},{"slug":"article-16-provider-obligations","label":"Article 16: the twelve duties of a provider of a high-risk AI system","summary":"Article 16 is the summary list of duties for providers: twelve points that route onward to the quality management system, the documentation, the logs, the conformity assessment, the EU declaration of conformity, the CE marking, the registration, corrective actions and accessibility requirements.","legal_status":"upcoming","deadline_at":"2027-12-02T00:00:00.000Z","high_risk_regime_from":null,"human_page":"https://www.praxikon.com/en/verplichtingen/article-16-provider-obligations","api":"https://www.praxikon.com/api/v1/obligations?lang=en","official_source":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","citations":[{"kind":"official_fact","statement":"Article 16 requires providers of high-risk AI systems to do twelve things. They must ensure their systems comply with the requirements of Chapter III, Section 2 (point (a)); indicate on the system or, where that is not possible, on its packaging or accompanying documentation, their name, registered trade name or registered trade mark and the address at which they can be contacted (point (b)); have a quality management system in place complying with Article 17 (point (c)); keep the documentation referred to in Article 18 (point (d)); keep the automatically generated logs referred to in Article 19 when under their control (point (e)); ensure the system undergoes the conformity assessment procedure referred to in Article 43 prior to being placed on the market or put into service (point (f)); draw up an EU declaration of conformity in accordance with Article 47 (point (g)); affix the CE marking in accordance with Article 48 (point (h)); comply with the registration obligations referred to in Article 49(1) (point (i)); take the necessary corrective actions and provide the information required under Article 20 (point (j)); upon a reasoned request of a national competent authority, demonstrate conformity with the requirements of Section 2 (point (k)); and ensure the system complies with the accessibility requirements of Directives (EU) 2016/2102 and (EU) 2019/882 (point (l)).","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 16(a)-(l)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"editorial_interpretation","statement":"Article 16 reads like a table of contents and is therefore often planned as a single roadmap line. It is twelve separate duties with widely differing lead times: building a quality management system takes months, affixing a CE marking takes a day. The bigger trap sits in Article 25(1): anyone who puts their own brand on an existing high-risk system, substantially modifies it, or changes the intended purpose of a non-high-risk system so that it becomes high-risk counts as a provider and inherits all twelve points without ever having built anything. In the branding scenario of point (a) this applies without prejudice to contractual arrangements stipulating that the obligations are otherwise allocated, but you must have made and be able to show those arrangements in advance. In practice this catches parties that white-label AI or apply a general-purpose model to an Annex III use case.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 16(a)-(l)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"recommended_action","statement":"First determine whether you are a provider or whether Article 25 makes you one, then work out the twelve points as twelve separate work packages with an owner and a date. Start with points (c) and (f), because they set the lead time of the whole track.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 16(a)-(l)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}]},{"slug":"article-10-data-governance","label":"Article 10: data and data governance","summary":"Quality and governance requirements for training, validation and test data of high-risk AI.","legal_status":"upcoming","deadline_at":"2027-12-02T00:00:00.000Z","high_risk_regime_from":null,"human_page":"https://www.praxikon.com/en/verplichtingen/article-10-data-governance","api":"https://www.praxikon.com/api/v1/obligations?lang=en","official_source":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","citations":[{"kind":"official_fact","statement":"Article 10 requires datasets appropriate to the intended purpose, with governance over origin and composition, attention to representativeness, errors and completeness, and examination of possible bias with appropriate measures.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 10(1)-(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"official_fact","statement":"For the Annex III route this requirement applies from 2 December 2027; for high-risk AI in regulated products (Annex I) from 2 August 2028.","source_id":"praxikon:eu:ai-act:source:reg-eu-2026-1744","source_locator":"Amended Article 113 application dates","source_url":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","eli":"http://data.europa.eu/eli/reg/2026/1744/oj"},{"kind":"editorial_interpretation","statement":"Today’s dataset choices determine whether compliance is feasible later: data bought or collected today without provenance records cannot be repaired in 2027.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 10(1)-(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"},{"kind":"recommended_action","statement":"Record origin and assumptions per dataset and include data quality as a requirement in every AI or data procurement contract.","source_id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","source_locator":"Article 10(1)-(6)","source_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj"}]}],"conditional":[{"slug":"article-61-informed-consent","id":"praxikon:eu:ai-act:obligation:article-61-informed-consent","label":"Article 61: informed consent of test subjects for testing in real world conditions","status":"possibly_applies","source_locator":"Article 60(4), point (i), with Article 61(1)","addressee":"reader","human_page":"https://www.praxikon.com/en/verplichtingen/article-61-informed-consent"}],"sources":[{"id":"praxikon:eu:ai-act:source:reg-eu-2024-1689","title":"EU Artificial Intelligence Act 2024/1689","publisher":"European Parliament and Council","canonical_url":"https://eur-lex.europa.eu/eli/reg/2024/1689/oj","eli":"http://data.europa.eu/eli/reg/2024/1689/oj","source_version":"original-oj-2024-07-12","verified_at":"2026-08-08T00:00:00.000Z"},{"id":"praxikon:eu:ai-act:source:reg-eu-2026-1744","title":"Digital Omnibus on AI 2026/1744","publisher":"European Parliament and Council","canonical_url":"https://eur-lex.europa.eu/eli/reg/2026/1744/oj","eli":"http://data.europa.eu/eli/reg/2026/1744/oj","source_version":"official-journal-2026-07-24","verified_at":"2026-08-08T00:00:00.000Z"},{"id":"praxikon:eu:ai-act:source:commission-ai-literacy-qa","title":"AI literacy questions and answers","publisher":"European 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measures","summary":"Determine for each role, system and context which combination of instruction, guidance, practice or training is appropriate."},{"label":"Justify the Article 6(3) exception against each individual condition","summary":"Name which of the four Article 6(3) conditions you invoke, with facts, and separately justify why the system poses no significant risk of harm to health, safety or fundamental rights and does not materially influence the outcome of decision making."},{"label":"Assign human oversight and give those people a mandate","summary":"Name, per high-risk system, who exercises oversight, and ensure that person has the competence, training, authority and support to actually set the output aside."}],"evidence":[{"label":"AI literacy measures record","summary":"Versioned record of roles, context, measures, participation or instruction and review moments.","url":null},{"label":"Article 49(2) registration record for the system assessed as not high-risk","summary":"Proof that the system for which you invoke the Article 6(3) exception is registered as Article 49(2) requires, with the registration number linked to the underlying assessment.","url":null},{"label":"Deployment dossier: logs, worker information and information to affected persons","summary":"The dossier that shows you retain the logs, that you informed workers and their representatives in time, and that the people about whom decisions are made are aware of it.","url":null}],"guidance":[{"label":"No mandatory course format, no certificate, no exam and no AI officer","statement":"The Commission Q&A on AI literacy states that there is no one size fits all when it comes to AI literacy and that no strict requirements or mandatory trainings are imposed. On certification, the Q&A states literally that there is no need for a certificate and that organisations can keep an internal record of trainings or other guiding initiatives. On assessment, it states that Article 4 of the AI Act does not entail an obligation to measure the AI knowledge of employees. On governance, it states that no specific governance structure is mandated to comply with Article 4, so that unlike the data protection officer under the GDPR, no AI officer needs to be appointed. On the level, the Q&A states that following the Digital Omnibus amendment AI literacy remains an obligation for providers and deployers of AI systems, but that no specific or sufficient level is mandated and that the Regulation does not require guaranteeing any specific level of AI literacy of any individual. Against that, the Q&A states that simply relying on the AI systems' instructions for use or asking staff to read them might be ineffective, and that organisations should take into account general AI understanding within the organisation, whether they are a provider or a deployer, the risks associated with the systems deployed, staff knowledge gaps considering technical knowledge, experience, education and training, and contextual factors such as sector, purpose and affected populations. The Q&A further states that organisations may implement different levels of training or learning approaches depending on knowledge, experience, education and role, and that staff with a degree or experience in AI development are normally considered AI literate, while the organisation must still verify that those persons understand the specific AI systems of the organisation, know how to deal with them and are aware of all risks.","source_locator":"Commission Q&A on AI literacy, sections on required level, training formats, certificates, assessment of knowledge and governance structures (consulted 9 August 2026)"},{"label":"Article 4 reaches beyond your own staff, and the national supervisor enforces it","statement":"The Commission Q&A on AI literacy states that Article 4 applies to providers and deployers of AI systems and in addition to other persons dealing with the operation and use of AI systems on their behalf, covering persons broadly within the organisational remit, with a contractor, a service provider and a client given as examples. On clients, the Q&A states that they may need AI literacy depending on the specific risk, reasoning that affected persons should understand how decisions taken with the assistance of AI will have an impact on them. On geographic scope, the Q&A states that the AI Act's legal framework applies to both public and private actors inside and outside the EU as long as the AI system is placed on the Union market, used in the Union, or its use has an impact on people located in the EU. On supervision, the Q&A states that the supervision and enforcement of Article 4 is not with the AI Office but under the remit of national market surveillance authorities, and that supervision and enforcement began on 2 August 2026, while Article 4 itself entered into application on 2 February 2025. On sanctions, the Q&A states that national market surveillance authorities could impose penalties and other enforcement measures for infringements of Article 4, that this will be based on national laws that Member States were due to adopt by 2 August 2025, that any sanction must be proportionate and based on the individual case taking into account factors such as the nature and gravity of the infringement and its intentional or negligent character, and that sanctions are more likely if there is proof of an incident due to a lack of appropriate training and guidance. Article 4 is not listed in the enumeration in Article 99(4) of the AI Act, which covers only Articles 16, 22, 23, 24, 26, 31, 33(1), (3) and (4), 34 and 50, so the level of any penalty for Article 4 follows from national law rather than from the Regulation's own ceilings. The Q&A further states that Article 4 reinforces the transparency provisions of Article 13 and the human oversight provisions of Article 14 and indirectly contributes to the protection of affected persons, and that for deployers of high-risk systems the Article 26 obligation to ensure staff are trained to ensure human oversight is a distinct requirement; that requirement becomes applicable on 2 December 2027 for standalone Annex III systems and on 2 August 2028 for Annex I systems.","source_locator":"Commission Q&A on AI literacy, sections on target groups, geographic scope, supervision and enforcement, and sanctions (consulted 9 August 2026)"},{"label":"Article 6 has two separate routes to high-risk","statement":"The European Commission's draft guidelines on the classification of high-risk AI of 19 May 2026, which are expressly non-binding, state in paragraph (7) that an AI system is high-risk in two scenarios: first, where it is intended to be used as a safety component of a product, or is itself a product, covered by the Union harmonisation legislation listed in Annex I and required to undergo third-party conformity assessment; and second, where it falls within one of the use cases in the areas listed in Annex III. Paragraph (448) of those same draft guidelines notes that the Article 113 application dates have been postponed by the AI Omnibus to 2 December 2027 for the Article 6(2) route and 2 August 2028 for the Article 6(1) route.","source_locator":"Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II, paragraph (7); section V, paragraph (448)"},{"label":"Broadly positioned and general purpose AI systems: a disclaimer is not enough","statement":"According to the non-binding draft guidelines of 19 May 2026 on the classification of high-risk AI, paragraph (12) provides that where the instructions for use, contractual arrangements, terms of service, usage policy, promotional and sales materials or technical documentation present the AI system as broadly applicable across a generality of contexts and functions, and do not consistently limit its application or exclude high-risk uses, the system's intended purpose will be deemed to also encompass high-risk use cases and therefore qualify as high-risk. Under these draft guidelines this applies in particular where such uses are feasible and reasonably foreseeable given the system's functionalities and capabilities. The same paragraph states that merely asserting, for example in the terms of service, that high-risk uses are excluded is insufficient where the provider's overall presentation, examples or product positioning effectively provides for or promotes such uses, and that any limitations of use must be described clearly, concretely and coherently across all materials.","source_locator":"Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II.2, paragraph (12)"}],"examples":[{"label":"A three-part training for legal and public affairs staff","situation":"Booking.com built a three-part training for its legal and public affairs teams: first basic terminology and the difference between classic machine learning and language models, then how AI works inside the company, then the regulatory landscape and where it meets the law they already practise. The material was also released as a video and podcast series with subtitles and written handouts.","outcome":"This practice was submitted by the organisation itself to the Commission living repository. The repository collects and shares practices; it does not approve them or set them as a standard.","lesson":"The sequence is the interesting part: first the technology, then the organisation itself, and only then the law. Lawyers who reverse that order memorise the Regulation without being able to judge where their own systems land. That the training exists in several formats also helps to show it was genuinely reachable for everyone who needed it.","source_locator":"Living repository of AI literacy practices, practice submitted by the organisation concerned","provenance":"official"},{"label":"A trained AI contact person in every department at a telecom company","situation":"Fastweb operates more than ninety AI systems and formally appoints an AI-SPOC in every department, a trained point of contact for AI questions from that team. These people receive separate instruction on prohibited practices and high-risk systems and are allowed to run their department's AI risk assessment themselves.","outcome":"This practice was submitted by the organisation itself to the Commission living repository. The repository collects and shares practices; it does not approve them or set them as a standard.","lesson":"Do not copy a practice from this repository as is, since the Commission states that replication grants no automatic presumption of compliance; first test whether the setup fits your own systems and roles.","source_locator":"Living repository of AI literacy practices, practice submitted by the organisation concerned","provenance":"official"},{"label":"Weather simulation where machine learning approximates physical processes","situation":"A meteorological institute runs physics based weather models and uses machine learning to approximate complex atmospheric processes such as cloud microphysics and turbulence. The estimated values are then fed into the established physics model, which produces the actual forecast.","outcome":"The Commission guidelines on the definition of an AI system use this case to draw the line between software that does and does not fall under the regulation. The document is non-binding.","lesson":"That your model can infer from input does not by itself bring it within the definition, since the guidelines justify excluding such accelerating systems precisely because they do not transcend basic data processing.","source_locator":"Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)","provenance":"official"},{"label":"AI literacy in recruitment and onboarding at an insurer","situation":"Gjensidige Forsikring gives all employees a mandatory e-learning as a baseline and builds role-based depth on top: analysts get model risk and data governance, claims handlers get training on the systems they operate themselves. Where relevant, AI literacy is checked during recruitment and training on AI systems is part of onboarding.","outcome":"This practice was submitted by the organisation itself to the Commission living repository. The repository collects and shares practices; it does not approve them or set them as a standard.","lesson":"Decide whom you train using the wording the document quotes: Article 4 names your own staff as well as anyone using the systems on your behalf.","source_locator":"Living repository of AI literacy practices, practice submitted by the organisation concerned","provenance":"official"}],"standards":[{"label":"ISO/IEC 5259 series: data quality for analytics and machine learning","summary":"The five-part international series on data quality, in practice the most usable structure for the Article 10 data governance dossier.","statement":"The ISO/IEC 5259 series (Artificial intelligence: Data quality for analytics and machine learning) comprises five parts: part 1 (overview, terminology and examples), part 2 (data quality measures), part 3 (data quality management requirements and guidelines) and part 4 (data quality process framework), all published in 2024, plus part 5 (data quality governance framework), published in February 2025. CEN-CENELEC has adopted parts as European standards, including EN ISO/IEC 5259-4:2025 and EN ISO/IEC 5259-3:2025. No part is cited in the Official Journal, so no presumption of conformity under Article 40 arises. The deliverable intended to do so for Article 10 is prEN 18284."},{"label":"prEN 18284: quality and governance of datasets in AI","summary":"The draft European standard operationalising the Article 10 data governance requirements for training, validation and testing data.","statement":"prEN 18284 (Artificial intelligence: Quality and governance of datasets in AI) is the JTC 21 deliverable under M/613 for Article 10 of the AI Act, which sets requirements for the training, validation and testing datasets of high-risk AI systems. As at June 2026 the deliverable was at the drafting stage. It has not yet been published as an EN and is not cited in the Official Journal."}],"definitions":[],"answer_page":"https://www.praxikon.com/en/antwoord/trustworthy-ai-altai","follow_up_questions":[{"question":"What does Article 4 AI literacy concretely require from us?","url":"https://www.praxikon.com/en/antwoord/ai-geletterdheid-regelen"},{"question":"Are we a provider or a deployer under the AI Act?","url":"https://www.praxikon.com/en/antwoord/aanbieder-of-gebruiksverantwoordelijke"},{"question":"Do we need to appoint an AI officer or AI compliance officer?","url":"https://www.praxikon.com/en/antwoord/ai-compliance-officer"}],"disclaimer":"General interpretation, not legal advice. The official source remains authoritative.","methodology":"https://www.praxikon.com/en/methodologie"}