Is the ALTAI / trustworthy AI assessment mandatory under the AI Act?

education

A three-part training for legal and public affairs staff

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.

Provenance: 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.

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.

Living repository of AI literacy practices, practice submitted by the organisation concerned

education

A trained AI contact person in every department at a telecom company

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.

Provenance: 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.

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.

Living repository of AI literacy practices, practice submitted by the organisation concerned

education

Weather simulation where machine learning approximates physical processes

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.

Provenance: 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.

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.

Commission Guidelines C(2025) 5053 final, 29.7.2025, borderline cases under the definition in Article 3(1)

education

AI literacy in recruitment and onboarding at an insurer

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.

Provenance: 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.

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.

Living repository of AI literacy practices, practice submitted by the organisation concerned

No mandatory course format, no certificate, no exam and no AI officer

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.

Commission Q&A on AI literacy, sections on required level, training formats, certificates, assessment of knowledge and governance structures (consulted 9 August 2026)

Article 4 reaches beyond your own staff, and the national supervisor enforces it

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.

Commission Q&A on AI literacy, sections on target groups, geographic scope, supervision and enforcement, and sanctions (consulted 9 August 2026)

Article 6 has two separate routes to high-risk

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.

Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II, paragraph (7); section V, paragraph (448)

Broadly positioned and general purpose AI systems: a disclaimer is not enough

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.

Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, section II.2, paragraph (12)

ISO/IEC 5259 series: data quality for analytics and machine learning

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.

prEN 18284: quality and governance of datasets in AI

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.