Transparency, User Info & Labelling — Foundations
Transparency, User Info & Labelling
EU AI Act Compliance Foundations EU/UK aligned
+ On this page
Key takeaways
- Tell people clearly when AI is used, what it can/can’t do, and how to reach a human.
- Keep the exact wording versions and comprehension test results as evidence.
AI use disclosure
- Inline badges; first-use interstitials; channel-specific disclosures (chat, email, voice, web forms).
Instructions of use
- Intended purpose; known limitations; do/don’t; when to escalate to a human; data entry cautions.
Explainability & limits
- Confidence/uncertainty cues; source previews for RAG; counterfactual examples where appropriate.
Provenance & watermarking
- Image/audio/video labelling; watermark policies; disclosure for synthetic media and UGC moderation.
Content & UGC disclosures
- Help text near inputs; clear consent prompts for training/feedback reuse; link to policy and opt-out.
Accessibility & language
- Reading level; localisation; screen-reader tags; high-contrast modes; plain-language alternatives.
Privacy notices
- Specific to AI features: lawful basis, retention, model feedback handling, sharing, DSRs.
Children & vulnerable users
- Age-appropriate design; parental controls; simplified disclosures; safety rail enhancements.
Channel-specific patterns
- Web, mobile, email, SMS, IVR/voice — each needs its own placement, copy length, and escalation path.
User testing & comprehension
- A/B wording; comprehension questions; misinterpretation logging; change control for copy.
Records & audit
- Archive of versions; screenshots; decision/rationale for wording changes; test results.
Transparency checklist
- Disclosures live in all channels; instructions of use linked; records archived; evidence index updated.
© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 05 Nov 2025 • This page is general guidance, not legal advice.
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