The label is the part you own
How we organise
Transparency obligations under Article 50 of the AI Act (European Commission)
Article 50 of the EU AI Act has applied since 2 August 2026, and the European Commission has now published its answers on what the duty covers. Providers must build systems that tell people they are talking to AI, and must watermark generative output so that a machine can detect it. Deployers carry a separate duty. An organisation that publishes AI-generated text on a matter of public interest, without human review or editorial control, must label it, and deepfakes must be labelled too. A provider outside the EU is caught if the output is used inside it. Fines reach 3 per cent of worldwide turnover.
Structure: the deployer duty is the half that lands on an ordinary organisation, and we keep finding it assigned to nobody. A provider knows which of its systems generate text. Whoever publishes that text usually does not, because the choice was made by whoever happened to be writing. The duty asks for something no policy document supplies, which is a current answer to what was machine-written and went out unreviewed. That answer lives in the publishing workflow rather than in the AI policy.
Take everything your organisation published this week on a matter of public interest, and against each item name the person who reviewed it before it went out. Every blank is the work Article 50 is asking for.
How we build
Control and complexity: tension in systems design (Fred Hébert)
Hébert sets two ways of designing a system against each other. The first breaks the system into parts and steers the whole from the top. The second treats the system as too interconnected for that, and works on the interactions so that good behaviour appears without being specified. He then lists practices that serve either stance, code review among them. A review can hunt defects, or it can spread awareness across teams. Install a practice for control, he argues, and practitioners will often run it for adaptation anyway, and that second use is lost when the practice tightens.
Architecture: this is why a practice copied from another organisation so often disappoints. The artefact travels and the stance does not. Two organisations run the same review, and the second is getting a benefit nobody wrote down. Harden it into a gate and the benefit leaves, while the change record shows only that a control was strengthened. So the question to put to any control you are about to add is what adaptation is currently happening around the thing you mean to constrain.
Pick the practice your organisation is trying hardest to enforce this quarter. Ask two people who perform it what they get out of it. If neither answer matches the reason on the policy page, you are about to remove the reason it works.
How we assure
How Claude’s text watermark works (Anthropic)
Anthropic will watermark the text of future Claude models, and has set out what the watermark does. It uses SynthID-Text, published by Google DeepMind, which changes only where the randomness comes from when the model picks between words that would serve equally well. Readers cannot detect it, and it adds no tokens and no cost. The company is then unusually plain about the limits. The watermark answers one question, which is how likely it is that Claude was involved. It cannot show that a text is human-written, cannot separate writing from heavy editing, and goes weak on short passages, on factual passages where the wording is forced, and on code. A full rewrite removes it.
Ethics: a duty to watermark will be read as a duty that settles authorship, and it does not. Watermark evidence is probabilistic and specific to one provider. Its absence proves nothing, because a different model leaves a different watermark or none. Its presence cannot separate the writer who prompted a draft from the writer who edited one heavily. Write detection into a policy, judge people by the result, and you have built a disciplinary process on evidence never designed to carry it. We would put this evidence to provenance across a body of published work, and never to a verdict on one writer.
Read your organisation’s AI policy for the sentence that treats a detection result as proof, and rewrite it so that a watermark result is one input and never the finding. Then name who decides when it disagrees with a colleague.