EU AI transparency rules make process notes practical

What happened

On August 2, 2026, Article 50 transparency obligations under the EU AI Act began applying. The AI Act Service Desk's Article 113 page says the regulation applies from that date, with some earlier and later provisions carved out separately.

The European Commission describes these Article 50 obligations as covering transparency for providers and deployers of generative AI systems. In practical terms, the Commission says the rules relate to marking and detection of AI-generated content, labelling deepfakes, and labelling certain AI-generated publications.

The Verge reported on August 3 that the rules are now in effect and summarized the split between providers and deployers. Providers must design AI systems so people know when they are interacting with AI unless that is obvious, and outputs such as synthetic audio, image, video, and text must be marked in a machine-readable way where the rule applies. Deployers must label deepfake image, audio, or video content that is made to look authentic, and must disclose certain AI-generated or manipulated text published to inform the public on matters of public interest.

There are limits and exceptions. Article 50 says the text disclosure obligation does not apply where AI-generated content has undergone human review or editorial control and a natural or legal person holds editorial responsibility for the publication. The Commission's code of practice is voluntary, but the underlying Article 50 transparency requirements are legal obligations.

Legal scope and editorial practice

This is not legal advice, and it is not a claim that every REC customer or every expert-content workflow falls under the same EU obligation. Teams need counsel for their own jurisdiction, product, audience, and use case.

It is also not a reason to treat every AI assist as a public warning label. Article 50 draws lines between providers, deployers, media types, deepfakes, public-interest text, assistive editing, and human-reviewed editorial work.

A label does not explain whether an article came from a recorded interview, who supplied an example, which sources were checked, or who reviewed the final wording.

Record AI use while the work is in progress

Record AI use and source decisions while preparing the work, so editors can check them before publishing.

A useful process note records roles in a few lines. What source material entered the workflow? What did AI help prepare, summarize, structure, or transform? What did the person say from direct knowledge? What did a human review? Who is responsible for the published claim?

That record is useful even when no public label is required. It helps an editor decide whether the content is ready. It helps a founder avoid approving a claim that sounds stronger than the source. It helps a team answer a client, platform, or reader question without reconstructing the whole workflow from memory.

Article 50's human-review and editorial-responsibility language also raises a practical review question for teams: who checked the public claim against the source?

Why recorded answers help

A research-guided video interview gives the process note a real source to point at. The person answers on camera. The transcript records the answer. The team can trace a clip, caption, article section, or social post back to something the person actually said.

That does not make the final asset automatically correct. A recording can be vague. A transcript can contain errors. An edit can remove the caveat that made the answer responsible. Human review is still work.

The advantage is that the review has evidence. Instead of asking whether an AI-written paragraph sounds plausible, the editor can ask whether the paragraph fairly represents the recorded answer. Instead of relying on a label that says AI was used, the team can explain how AI was used: research support, question preparation, transcription, highlight suggestions, caption options, or formatting.

A short clip, quote, or search summary often appears without its original context. Keep the source trail internally so you can check what the excerpt leaves out.

A practical process note

Before publishing an AI-assisted expert article, clip, or post, write five plain fields.

First, source: name the recording, transcript, document, product note, research link, or firsthand example that supports the central claim.

Second, AI role: state the specific jobs AI performed. Useful examples are research preparation, question generation, transcript cleanup, highlight discovery, outline drafting, caption options, or formatting. Avoid vague language such as AI-powered if it hides the actual work.

Third, human contribution: name what the expert supplied that the system could not know by itself: the example, judgment, caveat, definition, decision rule, objection, or tradeoff.

Fourth, review: record who checked the final claim against the source and whether the published wording preserves the speaker's meaning.

Fifth, public disclosure: decide whether the audience, platform, customer, or law requires a visible label or note. If the answer is yes, keep it factual. If the answer is no, keep the internal note anyway.