Article 50 is product work, design work, and evidence work.
Teams need to know where disclosures appear, when machine readable marking is required, and what proof to keep.
Structured answer sets in this page tree.
Cited legal and guidance references.
Transparency is one of the most visible parts of the AI Act because users, deployers, buyers, and authorities can all see when it fails. The law covers several different situations, so the first implementation step is to map each trigger to the product or content surface where it actually appears.
Article 50 requires providers of AI systems intended to interact directly with natural persons to inform those persons that they are interacting with an AI system, unless the use is obvious from the perspective of a reasonably well informed and observant person in context. This is a user journey question, not a generic site footer question.
Deployers of emotion recognition systems and biometric categorisation systems also have to inform natural persons exposed to their operation, subject to the law enforcement exception structure in the Regulation.
Providers of systems generating synthetic audio, image, video, or text content must ensure outputs are marked in a machine readable format and detectable as artificially generated or manipulated, where the article requires it. The law also says the technical solution should be effective, interoperable, robust, and reliable as far as technically feasible, taking account of content type, implementation cost, and the state of the art.
This is a technical implementation problem as much as a legal one. Teams should define where the marking is created, how it survives downstream handling, and how QA verifies it.
Deployers of systems that generate or manipulate image, audio, or video content constituting a deepfake must disclose that the content has been artificially generated or manipulated. For artistic, satirical, fictional, or analogous works, the disclosure must still exist but can be adapted so it does not undermine the normal display or enjoyment of the work.
AI generated or manipulated text published to inform the public on matters of public interest also needs disclosure unless it has undergone human review or editorial control and a natural or legal person holds editorial responsibility for the publication.
The strongest transparency programs treat disclosures as reusable interface components with design system rules, accessibility review, and regression testing. That improves consistency and reduces the risk that a new feature quietly removes or hides a required notice.
Evidence should show where the disclosure appears, which copy version is live, what technical marking is used, and what event triggers re review.
Assessment Autopilot can take EU AI Act (Regulation (EU) 2024/1689) Transparency and labeling from turning this guidance into an operational assessment workflow to a reusable workflow inside Sorena. Teams working on EU AI Act (Regulation (EU) 2024/1689) can keep owners, evidence, and next steps aligned without copying this guide into separate documents.
Start from EU AI Act (Regulation (EU) 2024/1689) Transparency and labeling and turn the guidance into owned tasks, evidence requests, and review checkpoints.
Review your current process, evidence gaps, and next steps for EU AI Act (Regulation (EU) 2024/1689) Transparency and labeling.