Implementing Article 50 — a practical guide

Pre-work — get the inventory honest

Article 50 compliance starts with knowing every AI system that touches a user. That's harder than it sounds — the chatbot your support team installed last quarter, the AI accessibility overlay your web team added six months ago, the AI summariser baked into a vendor's product.

1. Run a quick discovery sweep. Ask each team lead: "is there an AI tool customers see or get a response from?" Cross-check with your CMS, your tag manager, your subscription expense lines.
2. List the systems in one place. Klarvo's Discover step is built exactly for this; a spreadsheet works for the first pass.
3. For each, tag the deployment surface — chatbot, content publication, biometric inference, deepfake. The disclosure pattern depends on the surface.

Pattern 1 — Chatbot disclosure (Article 50(1))

The disclosure goes at the first interaction, in a way that is clear and distinguishable. The shortcuts that work:

• A short line at the very top of the chat thread: "You're chatting with an AI assistant. For complex queries, ask to be transferred to a person."
• An identifying name + icon for the AI agent — "Mira, our AI assistant" — that visibly distinguishes it from a human agent.
• A timestamped sample of the greeting message stored as evidence.

Pattern 2 — AI-generated content (Article 50(2))

Article 50(2) is on the provider of the system to make AI output machine-detectable (watermarks, content credentials, metadata). As a deployer, your job is:

• Never strip those markings.
• Add a visible cue where context allows — "Drafted with AI assistance and edited by [Name]" on AI-drafted blog posts; "Illustration: AI-generated" on AI images.
• Document the editorial process: who reviewed the AI output, who signed off, what changed.

Pattern 3 — Emotion or biometric inference (Article 50(3))

If you process biometric inputs to categorise people (race, age, gender, etc.) or infer emotion outside the prohibited contexts (workplace / education), you must:

• Inform the affected person before the inference happens.
• Have a valid GDPR lawful basis (consent or otherwise) — emotion / biometric categorisation is Article 9 special-category data and needs a corresponding Article 9 condition.
• Document the lawful basis in the privacy notice and in the evidence vault.

Pattern 4 — Deepfake labelling (Article 50(4))

Any AI-generated content that resembles real people/places/events and could appear authentic must carry an explicit "AI-generated or manipulated" label.

• Visible overlay on image/video (top corner, "AI" badge).
• Filename prefix and metadata field carrying the same marker (content credentials).
• Limited carve-outs for satire, parody, certain journalistic uses — when relying on a carve-out, document the rationale.

The team ritual that keeps you compliant

A one-line change in the chatbot copy or a new AI feature in a vendor product can re-open a compliance gap silently. Two practices catch this cheaply:

1. Quarterly Article 50 review — 30 minutes, run by whoever owns compliance. Walk through every AI system on the inventory; confirm the disclosure is live; capture a fresh screenshot. Klarvo can prompt this on a schedule.
2. "AI flag" in product/marketing change-control — any change to a vendor tool or website surface flags whether it adds an AI capability. If yes, run the Transparency Checker before deploy.

The evidence pack the regulator wants

• The AI inventory, with classification per system.
• The Article 50 disclosure text for each system, with deployment date and surface.
• A timestamped screenshot of the live disclosure (refreshed quarterly).
• The lawful-basis note for any biometric / emotion inference systems.
• The change-log of any disclosure-relevant changes (when wording changed, when a new system was added).

Klarvo's evidence pack export bundles all of the above into a single branded HTML document you can hand to an auditor or print to PDF — included on every paid tier, watermarked on the Free tier.