Does our system fall under the definition of an AI system (Article 3)?

recruitment and selection

Candidate recommendation that automatically becomes a decision

An employer uses a system that ranks applicants and recommends a candidate to hire. In one setup a recruiter weighs that recommendation in their own assessment; in the other the outcome is applied automatically and a candidate is rejected without anyone looking at it.

Provenance: The Commission draft guidelines of 19 May 2026 address this case when determining whether an application falls under Annex III. The document is a consultation version: non-binding and not yet final.

Assess a recruitment system on its intended purpose rather than on whether a recruiter reviews the output, because adding or removing human involvement does not change its high-risk classification.

Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III

biometrics and identification

Face comparison at the border gate: verification or identification

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.

Provenance: 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.

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.

Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III, paragraph (136)

recruitment and selection

A CV filter that ranks applicants

An employer has an external recruitment system score and rank every incoming application, after which recruiters only review the top twenty percent by hand. The vendor puts the system on the market under its own name, and the employer uses it in its own selection process.

Provenance: The Commission draft guidelines of 19 May 2026 address this case when determining whether an application falls under Annex III. The document is a consultation version: non-binding and not yet final.

Recruiters keeping the final say does not help you, because once the system scores or ranks applicants and thereby shapes the shortlist it stays high-risk and no exemption applies.

Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III

education

Application file handling at an educational institution

An educational institution uses AI for application file handling: indexing, searching, text and speech processing, translation of documents submitted with applications, and extracting, transforming and organising the collected data into a usable format.

Provenance: The Commission draft guidelines of 19 May 2026 address this case when determining whether an application falls under Annex III. The document is a consultation version: non-binding and not yet final.

Indexing, searching, translating and reorganising application files remains preparatory work, as long as the system leaves the substantive judgment on the application entirely to the institution.

Draft guidelines on high-risk AI classification, 19 May 2026, annex on Annex III

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)

High-risk does not mean prohibited, and not high-risk does not mean permitted

The draft guidelines of 19 May 2026 on the classification of high-risk AI, which are not binding, state in paragraph (3) that the fact an AI system is listed as an example in these guidelines does not mean its use should automatically be considered lawful, since such use would still need to comply with other applicable legislation, and in paragraph (4) that the scope of these guidelines is limited to whether an AI system is high-risk or not. In the Annex III chapter of this draft, paragraph (68) states that classifying systems as high-risk under Article 6(2) does not mean their use is prohibited, but that those systems are subject to appropriate requirements. Paragraphs (82) and (83) of this draft explain the wording in so far as their use is permitted under relevant Union or national law and state that falling within a use case does not necessarily mean the system may lawfully be used in those cases, that in addition to the prohibitions other provisions of Union or national law may restrict use, and that under Article 2(9) the AI Act applies without prejudice to rules on consumer protection, product safety and data protection.

Draft guidelines on high-risk AI classification (19 May 2026), General principles chapter, paragraphs (3) and (4); Annex III chapter, paragraph (68) and section 2.6, paragraphs (82) and (83)

Split and agentic architectures are assessed as a whole

The non-binding draft guidelines of 19 May 2026 provide in paragraphs 75, 76 and 90 that where several AI systems form part of a more complex whole and their combined intended purpose or joint outputs materially influence an individual decision, that configuration is treated as a single AI system for classification. The draft expressly states that split architectures are assessed as a whole to prevent circumvention by system design, that exemptions for individual modules do not apply where the overall configuration influences key aspects of the decision, and that this also extends to complex interconnected setups such as agentic AI systems whose linked actions jointly serve a high-risk purpose. Under the same draft, strictly procedural or preparatory functions do remain eligible for exemption where they are genuinely separable from the system and do not structure or feed outputs that materially influence the examination of an individual case.

Section IV.2.3, paragraphs 75 and 76, and section IV.2.7.1 paragraph 90

ISO/IEC 24029 series: assessment of the robustness of neural networks

ISO/IEC TR 24029-1:2021 (Assessment of the robustness of neural networks, Part 1: Overview) is a technical report mapping the topic and available assessment methods. ISO/IEC 24029-2:2023 (Part 2: Methodology for the use of formal methods) describes the application of formal methods in assessing robustness. The series is aimed at AI developers and users assessing robustness across the lifecycle. Neither part is cited in the Official Journal, so no presumption of conformity under Article 40 arises. For Article 15 the designated deliverable is prEN 18229-2, which as at June 2026 was still in drafting.

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 18229-2: AI trustworthiness framework part 2, accuracy and robustness

prEN 18229-2 (AI trustworthiness framework, Part 2: Accuracy and robustness) is the JTC 21 deliverable under M/613 for Article 15 of the AI Act, which requires high-risk AI systems to achieve an appropriate level of accuracy, robustness and cybersecurity and to declare accuracy metrics in the instructions for use. 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.

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.